Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T16:25:07.956Z Has data issue: false hasContentIssue false

On the Formation of the First Quasars

Published online by Cambridge University Press:  20 July 2017

Rosa Valiante*
Affiliation:
INAF/Osservatorio Astronomico di Roma, Via di Frascati 33, 00040 Monte Porzio Catone, Italy
Bhaskar Agarwal
Affiliation:
Zentrum für Astronomie, Institut für Theoretische Astrophysik, Universität Heidelberg, Albert-Ueberle-Straße 2, 69120 Heidelberg, Germany Department of Astronomy, 52 Hillhouse Avenue, Steinbach Hall, Yale University, New Haven, CT 06511, USA
Melanie Habouzit
Affiliation:
Center for Computational Astrophysics (CCA), Flatiron Institute, 162 5th Ave, New York, NY, USA
Edwige Pezzulli
Affiliation:
INAF/Osservatorio Astronomico di Roma, Via di Frascati 33, 00040 Monte Porzio Catone, Italy Dipartimento di Fisica, Universitá di Roma ‘La Sapienza’, P.le Aldo Moro 2, 00185, Roma, Italy
Rights & Permissions [Opens in a new window]

Abstract

Observations of the most luminous quasars at redshift z > 6 reveal the existence of numerous supermasssive black holes (>199 M) already in place about 12 billion years ago. In addition, the interstellar medium of the galaxies hosting these black holes are observed to be chemically mature systems, with metallicities (Z > Z) and dust masses (>108 M) similar to that of more evolved, local galaxies. The connection between the rapid growth of the first supermassive black holes and the fast chemical evolution of the host galaxy is one of the most puzzling issues for theoretical models. Here, we review state-of-the-art theoretical models that focus on this problem with particular emphasis on the conditions that lead to the formation of quasar seeds and their subsequent evolution at z ⩾ 6.

Type
Research Article
Copyright
Copyright © Astronomical Society of Australia 2017 

1 INTRODUCTION

Up to ~40 supermassive black holes (SMBHs) of >109 M have been observed till date, which are believed to power the optically bright quasars (>1047 erg s−1) observed at z > 5 (e.g. Jiang et al. Reference Jiang2009; Mortlock et al. Reference Mortlock2011; Wu et al. Reference Wu2015; Jiang et al. Reference Jiang, McGreer, Fan, Bian, Cai, Clément, Wang and Fan2015, Reference Jiang2016). How these BHs formed in a relatively short time scale, already 12 Gyr ago in the early Universe (≲ 700–800 Myr; e.g. Fan et al. Reference Fan2001, Reference Fan2004 and De Rosa et al. Reference De Rosa, Decarli, Walter, Fan, Jiang, Kurk, Pasquali and Rix2011, Reference De Rosa2014), is still an open question (e.g. Volonteri et al. Reference Volonteri2010; Natarajan Reference Natarajan2011; see Dolgov Reference Dolgov2016 for a discussion of their role in the formation of the early Universe).

Luminous (optically selected) quasars at high redshift, thus offer the most direct constraint on the evolution of the first SMBHs and serve a unique laboratory to study the earliest phases of galaxy formation and evolution as well as the properties of the early Universe. In the left panel of Figure 1, we show a collection of high-redshift (z > 3) SMBHs reported to date. Note that at z > 6, they are already as massive as the BHs observed at lower redshifts (z = 3–5) and in the local Universe (see e.g. Sani et al. Reference Sani, Marconi, Hunt and Risaliti2011; Kormendy & Ho Reference Kormendy and Ho2013). The two noteworthy record holders are ULAS J1120+0641 (J1120) and SDSS J0100+2802 (J0100), hosting the most distant (z ~ 7.1, Mortlock et al. Reference Mortlock2011), and the most massive (1.2 × 1010 M, Wu et al. Reference Wu2015) SMBHs ever observed at z > 6, respectively.

Figure 1. Left panel: Black hole mass as a function of redshift in z > 3 galaxies. References to the data are labelled and colour coded in the figure. Right panel: Bolometric luminosity as a function of the black hole mass for z ~ 6 quasars. Blue square, red circle, and magenta triangle represent quasars J0100, J1148, and J1120, respectively. Black empty data points are from the collection of high-z quasars by Wu et al. (Reference Wu2015). The green dashed line shows Eddington luminosity (Courtesy of F. Wang and X.B. Wu).

In the right panel of Figure 1, we show the bolometric luminosity as a function of the BH mass for the collection of z ⩾ 6 quasars presented by Wu et al. (Reference Wu2015).

The nuclei of these objects are actively accreting massive BHs, shining close to or above the Eddington luminosity (green dashed line). Coloured points show three objects of particular interest: the two record holders introduced above, J1120 (magenta triangle) and J0100 (blue square) and quasar SDSS J1148+5251 (red circle, hereafter J1148) which is one of the best studied quasar, discovered at z = 6.4 (Fan et al. Reference Fan2001). As it can be seen from the figure, J0100 is the most luminous quasar known at z > 6, with bolometric luminosity L Bol = L Edd ~ 4 × 1014 L(Wu et al. Reference Wu2015), making it four times brighter than J1148 (red circle) and seven times brighter than J1120 (magenta triangle).

The existence of these active and massive black holes (BHs) close to the reionisation epoch when the Universe was younger than ~1 Gyr, triggered a number of theoretical studies and deep, systematic observational campaigns that aimed at shedding light on their origin (e.g. Willott et al. Reference Willott2010; Wu et al. Reference Wu2015; Venemans et al. Reference Venemans2015; Bañados et al. Reference Bañados2016; Jiang et al. Reference Jiang2016 and references therein). This manuscript is part of a series of reviews on high-redshift BHs and it is intended to present state-of-the-art theoretical models for the formation and evolution of high-redshift SMBHs and their host galaxies.

2 OPEN QUESTIONS

An SMBH is born first as a much smaller seed BH, which then grows by accreting matter and merging with other BHs. Numerous studies have been devoted to explaining how and when these seed BHs and their host galaxies form. Here we briefly discuss the mostly debated issues related to the discovery and formation of distant quasars and their observed properties (see Gallerani et al. Reference Gallerani, Fan, Maiolino and Pacucci2017 for a recent review on the first quasars observed physical properties).

2.1. How and when did the z > 6 SMBHs form and the nature of their progenitors

The formation mechanism and properties of the first seed BHs are the subject of several studies which focus on three distinct scenarios (see e.g. Volonteri et al. Reference Volonteri2010; Natarajan Reference Natarajan2011; Latif & Ferrara Reference Latif and Ferrara2016 for complete reviews).

The first scenario relies on low-mass seeds, namely BHs of few tens to few hudreds solar masses, formed as remnants of Population III (Pop III) stars in the mass range [40–140] and >260 M (e.g. Madau & Rees Reference Madau and Rees2001; Abel, Bryan, & Norman Reference Abel, Bryan and Norman2002; Heger et al. Reference Heger, Fryer, Woosley, Langer and Hartmann2003; Volonteri, Haardt, & Madau Reference Volonteri, Haardt and Madau2003; Yoshida, Omukai, & Hernquist Reference Yoshida, Omukai and Hernquist2008; Latif et al. Reference Latif, Schleicher, Schmidt and Niemeyer2013b) up to ~1 000 M stars that may form at z > 20 (Hirano et al. Reference Hirano, Hosokawa, Yoshida, Omukai and Yorke2015).

On the other hand, intermediate mass, 103–104 M, BHs may arise from from stars and stellar-mass BHs collisions in dense clusters (e.g. Omukai, Schneider, & Haiman Reference Omukai, Schneider and Haiman2008; Devecchi & Volonteri Reference Devecchi and Volonteri2009; Regan & Haehnelt Reference Regan and Haehnelt2009b; Katz, Sijacki, & Haehnelt Reference Katz, Sijacki and Haehnelt2015; Davies, Miller, & Bellovary Reference Davies, Miller and Bellovary2011; Lupi et al. Reference Lupi, Colpi, Devecchi, Galanti and Volonteri2014; Yajima & Khochfar Reference Yajima and Khochfar2016).

Finally, a third SMBH formation channel has been proposed: high-mass seeds, forming in T vir ⩾ 104K halos, exposed to an intense H2 photo-dissociating ultra-violet (UV) flux (but see e.g. Spaans & Silk Reference Spaans and Silk2006, for a different scenario), via direct collapse (DC) of low metallicity gas clouds into 104–106 M BHs. Such a scenario has been explored in details by means of both analytic works (e.g. Loeb & Rasio Reference Loeb and Rasio1994; Bromm & Loeb Reference Bromm and Loeb2003; Eisenstein & Loeb Reference Eisenstein and Loeb1995; Volonteri & Rees Reference Volonteri and Rees2005a; Begelman, Volonteri, & Rees Reference Begelman, Volonteri and Rees2006; Lodato & Natarajan Reference Lodato and Natarajan2006; Spaans & Silk Reference Spaans and Silk2006; Ferrara et al. Reference Ferrara, Salvadori, Yue and Schleicher2014) and simulations (e.g. Wise, Turk, & Abel Reference Wise, Turk and Abel2008; Regan & Haehnelt Reference Regan and Haehnelt2009a, Reference Regan and Haehnelt2009b; Shang, Bryan, & Haiman Reference Shang, Bryan and Haiman2010; Inayoshi & Omukai Reference Inayoshi and Omukai2012; Regan, Johansson, & Wise Reference Regan, Johansson and Wise2014; Inayoshi, Omukai, & Tasker Reference Inayoshi, Omukai and Tasker2014; Becerra et al. Reference Becerra, Greif, Springel and Hernquist2015).

Another debated issue is related to the seed BH growth mechanism that is needed in order to explain z > 6 SMBHs.

Alvarez, Wise, & Abel (Reference Alvarez, Wise and Abel2009) pointed out that Pop III star remnants forming in minihalos at z > 15 do not grow efficiently in mass to become miniquasars (BHs with mass ~106 M). However, after merging with atomic cooling halos (i.e. halos with virial temperatures of ⩾104 K), the BH feedback may be able to inhibit star formation, thus leading to efficient accretion and growth of the BH.

In addition, if Pop III stars are less massive than expected, i.e. not exceeding 100 M (e.g. O’Shea & Norman Reference O’Shea and Norman2007; Hosokawa et al. Reference Hosokawa, Omukai, Yoshida and Yorke2011; Greif et al. Reference Greif, Springel, White, Glover, Clark, Smith, Klessen and Bromm2011; Stacy, Greif, & Bromm Reference Stacy, Greif and Bromm2012; Hirano et al. Reference Hirano, Hosokawa, Yoshida, Omukai and Yorke2015), the resulting BHs of ~20–60 M may receive a kick during their formation, ejecting them out of their host halos and thus preventing their subsequent growth (Whalen & Fryer Reference Whalen and Fryer2012). Moreover, because of their low mass, such BHs are not expected to settle in the galaxy centre. They would rather wander in the halo, without accreting gas (see e.g. Volonteri et al. Reference Volonteri2010, for a discussion).

Various studies suggest that BHs may evolve via uninterrupted gas accretion at the Eddington rate and/or episodic super-Eddington accretion phases, to grow up to billion solar masses, especially in the case of low-mass seeds (Haiman Reference Haiman2004; Yoo & Miralda-Escudé Reference Yoo and Miralda-Escudé2004; Shapiro Reference Shapiro2005; Volonteri & Rees Reference Volonteri and Rees2005a, Reference Volonteri and Rees2006a; Pelupessy, Di Matteo, & Ciardi Reference Pelupessy, Di Matteo and Ciardi2007; Tanaka & Haiman Reference Tanaka and Haiman2009; Johnson et al. Reference Johnson, Whalen, Li and Holz2013; Madau, Haardt, & Dotti Reference Madau, Haardt and Dotti2014; Volonteri, Silk, & Dubus Reference Volonteri, Silk and Dubus2015).

We refer the interested reader to reviews by Volonteri et al. (Reference Volonteri2010), Natarajan (Reference Natarajan2011), Volonteri & Bellovary (Reference Volonteri and Bellovary2012), Volonteri et al. (Reference Volonteri, Bogdanović, Dotti and Colpi2016a), Latif & Ferrara (Reference Latif and Ferrara2016), and Johnson & Haardt (Reference Johnson and Haardt2016), and references therein for details on the first seed BHs formation and feeding mechanisms.

The seeds of the first SMBHs are still elusive even to the most sensitive instruments that exist today, thus preventing us from putting observational constraints on their nature. A good example is the bright Lyα emitter CR7 observed at z ~ 6.6 (Matthee et al. Reference Matthee, Sobral, Santos, Röttgering, Darvish and Mobasher2015; Sobral et al. Reference Sobral, Matthee, Darvish, Schaerer, Mobasher, Röttgering, Santos and Hemmati2015; Bowler et al. Reference Bowler, McLure, Dunlop, McLeod, Stanway, Eldridge and Jarvis2016) where either Pop III stars (Sobral et al. Reference Sobral, Matthee, Darvish, Schaerer, Mobasher, Röttgering, Santos and Hemmati2015; Visbal, Haiman, & Bryan Reference Visbal, Haiman and Bryan2016; Dijkstra et al. Reference Dijkstra, Gronke and Sobral2016) or an accreting DCBH (Pallottini et al. Reference Pallottini2015; Hartwig et al. Reference Hartwig2016; Agarwal et al. Reference Agarwal, Johnson, Zackrisson, Labbe, van den Bosch, Natarajan and Khochfar2016b; Smith, Bromm, & Loeb Reference Smith, Bromm and Loeb2016; Smidt, Wiggins, & Johnson Reference Smidt, Wiggins and Johnson2016; Agarwal et al. Reference Agarwal, Johnson, Khochfar, Pellegrini, Rydberg, Klessen and Oesch2017a; Pacucci et al. Reference Pacucci, Pallottini, Ferrara and Gallerani2017) has been suggested as the primary constituent of its metal-poor component.

Although the observational signatures of seed BHs still remain unexplored, Pacucci et al. (Reference Pacucci, Ferrara, Grazian, Fiore, Giallongo and Puccetti2016) suggest a promising method to search for DCBH candidates in deep multi-wavelength surveys, based on photometric observations. By modelling the spectral energy distribution (SED) and colours of objects selected from the CANDELS/GOODS-S field catalogues (Guo et al. Reference Guo2013), they identify two X-ray-detected faint active galactic nuclei (AGN), 33160 and 29323 (Giallongo et al. Reference Giallongo2015) (but see also Weigel et al. Reference Weigel, Schawinski, Treister, Urry, Koss and Trakhtenbrot2015; Cappelluti et al. Reference Cappelluti2016; Vito et al. Reference Vito2016) as DCBHs prototypes at z ~ 6 and ~9.7, respectively.

The existence of such low-luminosity AGN at very high redshift, together with the recent reduction in the optical depth due to free electrons, τ e reported by the Planck Collaboration et al. (2016) has renewed the interest in the role of the first quasars in cosmological reionisation. Although the idea of quasars substantially contributing to, or even being the main responsible for, reionisation (e.g. Madau & Haardt Reference Madau and Haardt2015) is still highly debated (see e.g. D’Aloisio et al. Reference D’Aloisio, Sanderbeck, McQuinn, Trac and Shapiro2016) the recent discoveries strengthen the motivation for a better understanding of their demographics and origin.

2.2. What are the properties of high-z SMBHs hosts?

High-z quasars are predicted to be hosted in the most massive dark matter (DM) halos residing in over-dense environments (e.g. Overzier et al. Reference Overzier, Guo, Kauffmann, De Lucia, Bouwens and Lemson2009; Di Matteo et al. Reference Di Matteo, Khandai, DeGraf, Feng, Croft, Lopez and Springel2012; Angulo, Hahn, & Abel Reference Angulo, Hahn and Abel2013) However, clear observational evidences of such a scenario are still missing, as observations provide controversial results (e.g. Stiavelli et al. Reference Stiavelli2005a; Willott et al. Reference Willott, Crampton, Hutchings, Sawicki, Simard, Jarvis, McLure, Percival, Merloni, Nayakshin and Sunyaev2005; Wang, Malhotra, & Rhoads Reference Wang, Malhotra and Rhoads2005; Zheng et al. Reference Zheng2006; Kim et al. Reference Kim2009a; Utsumi et al. Reference Utsumi, Goto, Kashikawa, Miyazaki, Komiyama, Furusawa and Overzier2010; Husband et al. Reference Husband, Bremer, Stanway, Davies, Lehnert and Douglas2013; Simpson et al. Reference Simpson, Mortlock, Warren, Cantalupo, Hewett, McLure, McMahon and Venemans2014; Morselli et al. Reference Morselli2014; McGreer et al. Reference McGreer, Eftekharzadeh, Myers and Fan2016; Mazzucchelli et al. Reference Mazzucchelli, Bañados, Decarli, Farina, Venemans, Walter and Overzier2017; Balmaverde et al. Reference Balmaverde2017).

The quasar hosts are chemically evolved, metal and dust-rich galaxies. Although their metallicity is quite difficult to trace, constraints on the gas-phase elemental abundances in the interstellar medium (ISM) come from the detection of emission line ratios in broad- and the narrow-line regions (BLRs and NLRs, respectively).

Although BLRs are representative of a small fraction of the gas content, concentrated within the central region (104 M on parsec scales, close to the AGN), the observed emission line ratios, such as Feii/Mgii (e.g. Barth et al. Reference Barth, Martini, Nelson and Ho2003), NV/CIV (e.g. Pentericci et al. Reference Pentericci2002), (Si IV+OIV)/CIV (Nagao, Marconi, & Maiolino Reference Nagao, Marconi and Maiolino2006; Juarez et al. Reference Juarez, Maiolino, Mujica, Pedani, Marinoni, Nagao, Marconi and Oliva2009), and metal lines like Cii and Oi (e.g. Maiolino et al. Reference Maiolino2005; Becker et al. Reference Becker, Sargent, Rauch and Simcoe2006) trace up to ~7 Z metallicities (Nagao et al. Reference Nagao, Marconi and Maiolino2006; Juarez et al. Reference Juarez, Maiolino, Mujica, Pedani, Marinoni, Nagao, Marconi and Oliva2009) suggesting a fast evolution of the ISM chemical properties. By using emission line ratios as tracers, Jiang et al. (Reference Jiang, Fan, Vestergaard, Kurk, Walter, Kelly and Strauss2007) estimated gas metallicity of a sample of 5.8 < z < 6.3 quasars, powered by 109–1010 M SMBHs, finding values as high as ~4~Z.

A better proxy of the host galaxy ISM metallicity, on larger scales (comparable to the host galaxy size), is provided by NLRs. A mean gas-phase metallicity $Z_{\text{NLR}}=1.32^{+0.25 }_{-0.22} \text{ Z}_\odot$ is inferred from Civ/Heii and Ciii/Civ flux ratios in quasar, with no significant evolution up to z ~ 4 (Nagao et al. Reference Nagao, Marconi and Maiolino2006; Matsuoka et al. Reference Matsuoka, Nagao, Maiolino, Marconi and Taniguchi2009). Such super-solar metallicities are reminiscent of the star formation history (SFH) of the system (see e.g. Matsuoka et al. Reference Matsuoka, Nagao, Maiolino, Marconi and Taniguchi2009 and references therein) and can serve as a lower limit for the z ~ 6 quasar host galaxies.

Constraints on the cool/warm dust content come from the observations of far-infrared (FIR) and sub-millimetre (sub-mm) continuum radiation, while NIR and MIR observations may provide indications of the hot dust component (e.g. Jiang et al. Reference Jiang, Fan, Vestergaard, Kurk, Walter, Kelly and Strauss2007).

The observed ⩾1013 L quasar FIR luminosities are consistent with emission from dust with temperatures of the order of 30–60 K and masses >108 M (Bertoldi et al. Reference Bertoldi, Carilli, Cox, Fan, Strauss, Beelen, Omont and Zylka2003; Priddey et al. Reference Priddey, Isaak, McMahon, Robson and Pearson2003; Robson et al. Reference Robson, Priddey, Isaak and McMahon2004; Beelen et al. Reference Beelen, Cox, Benford, Dowell, Kovács, Bertoldi, Omont and Carilli2006; Wang et al. Reference Wang2008; Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014; Michałowski et al. Reference Michałowski, Murphy, Hjorth, Watson, Gall and Dunlop2010). From the same FIR luminosities, high star-formation rates (SFRs), ⩾1 000 M yr −1, can be inferred, suggesting that a large fraction of these systems has ongoing, highly efficient, star-formation activity (see e.g. Table 1 in Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014 and references therein).Footnote 1

2.3. Is there a stellar mass crisis?

The rapid enrichment by metals and dust at very high redshift discussed above suggests that quasar host galaxies could have undergone intense episodes of star formation. Similar chemical abundances are typically found in local galaxies which, however, evolved on longer time scales.

The estimated mean BH-stellar bulge mass ratio, M BH/M star, of z ~ 6 quasars is about 10 times higher than the one observed in the local Universe (e.g. Wang et al. Reference Wang2010, Reference Wang2013), suggesting that high-redshift BHs may have formed or assembled earlier than their host galaxies (e.g. Lamastra et al. Reference Lamastra, Menci, Maiolino, Fiore and Merloni2010; Venemans et al. Reference Venemans, Walter, Zschaechner, Decarli, De Rosa, Findlay, McMahon and Sutherland2016). Although this result could be strongly affected by observational selection effects (Lauer et al. Reference Lauer, Tremaine, Richstone and Faber2007; Volonteri & Stark Reference Volonteri and Stark2011) and large uncertainties in the estimation of the mass and size of the stellar bulge (Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014; Pezzulli, Valiante, & Schneider Reference Pezzulli, Valiante and Schneider2016), it is difficult to explain how the ISM has been enriched to chemical abundances similar to that of local galaxies, albeit with ≲ 10% of the stars (Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011; Calura et al. Reference Calura, Gilli, Vignali, Pozzi, Pipino and Matteucci2014; Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014).

2.4. What is the role of BH feedback?

It is expected that galaxy-scale winds, triggered by the large amount of energy released in the BH accretion process, play a crucial role in regulating the BH-host galaxy co-evolution, shaping the SFH and BH accretion history itself (e.g. Silk & Rees Reference Silk and Rees1998; Granato et al. Reference Granato, De Zotti, Silva, Bressan and Danese2004; Di Matteo, Springel, & Hernquist Reference Di Matteo, Springel and Hernquist2005; Springel, Di Matteo, & Hernquist Reference Springel, Di Matteo and Hernquist2005a; Ciotti, Ostriker, & Proga Reference Ciotti, Ostriker and Proga2009, Reference Ciotti, Ostriker and Proga2010; Hopkins & Elvis Reference Hopkins and Elvis2010; Zubovas & King Reference Zubovas and King2012).

Indeed, massive and fast large scale gas outflows, associated to quasar activity, have been observed in local and high-redshift quasars (Feruglio et al.; Reference Feruglio, Maiolino, Piconcelli, Menci, Aussel, Lamastra and Fiore2010, Reference Feruglio2015; Alatalo et al. Reference Alatalo2011; Aalto et al. Reference Aalto, Garcia-Burillo, Muller, Winters, van der Werf, Henkel, Costagliola and Neri2012; Alexander et al. Reference Alexander, Swinbank, Smail, McDermid and Nesvadba2010; Nesvadba et al. Reference Nesvadba2010, Reference Nesvadba, Polletta, Lehnert, Bergeron, De Breuck, Lagache and Omont2011, Maiolino et al. Reference Maiolino2012; Cano-Díaz et al. Reference Cano-Díaz, Maiolino, Marconi, Netzer, Shemmer and Cresci2012; Farrah et al. Reference Farrah2012; Trichas et al. Reference Trichas2012; Carniani et al. Reference Carniani2016). At z > 6, a massive gas outflow has been inferred from observations of [Cii] emission line in J1148, revealing an outflow rate ⩾2 000–3 000 M yr−1 (Maiolino et al. Reference Maiolino2012; Cicone et al. Reference Cicone2015).

However, there are still open issues like what is the outflow powering mechanism, what are the effects of BH feedback on the host galaxy, how can the observed strong outflows and starbursts be simultaneously sustained? Although there are hints of star formation being quenched by quasar feedback at high redshift (Cano-Díaz et al. Reference Cano-Díaz, Maiolino, Marconi, Netzer, Shemmer and Cresci2012; Farrah et al. Reference Farrah2012; Trichas et al. Reference Trichas2012; Carniani et al. Reference Carniani2016), it is unclear if such feedback is able to completely suppress star formation in galaxies (Peng, Maiolino, & Cochrane Reference Peng, Maiolino and Cochrane2015). On the other hand, it has been pointed out that AGN-driven positive feedback (Zinn et al. Reference Zinn, Middelberg, Norris and Dettmar2013; Cresci et al. Reference Cresci2015) which triggers or enhances star formation, may be as important as quenching mechanisms in galaxy formation (e.g. Gaibler et al. Reference Gaibler, Khochfar, Krause and Silk2012; Wagner, Umemura, & Bicknell Reference Wagner, Umemura and Bicknell2013; Silk Reference Silk2013; Bieri et al. Reference Bieri, Dubois, Silk and Mamon2015).

3 THEORETICAL MODELS

In the following sections, we review the results of state-of-the art theoretical models for the formation of the first BHs, the properties of the environment in which they form and the evolution of their host galaxies. We focus on models in which the evolution of the baryonic component of galaxies is followed by means of analytic prescriptions linked to their host DM halo properties. In particular, we discuss following two complementary approaches adopted to describe DM halos:

Pure semi-analytic techniques are commonly adopted to shed light either on the early gas enrichment with metals and dust in the high-redshift ISM (Hirashita & Ferrara Reference Hirashita and Ferrara2002; Morgan & Edmunds Reference Morgan and Edmunds2003; Dwek, Galliano, & Jones Reference Dwek, Galliano and Jones2007; Valiante et al. Reference Valiante, Schneider, Bianchi and Andersen2009; Gall et al.; Reference Gall, Hjorth and Andersen2011a, Reference Gall, Andersen and Hjorth2011b; Dwek & Cherchneff Reference Dwek and Cherchneff2011; Mattsson Reference Mattsson2011; Pipino et al. Reference Pipino, Fan, Matteucci, Calura, Silva, Granato and Maiolino2011; Calura et al. Reference Calura, Gilli, Vignali, Pozzi, Pipino and Matteucci2014) or on the origin of the first SMBHs and the resulting BH-host galaxy scaling relations (e.g. Volonteri et al. Reference Volonteri, Haardt and Madau2003, Reference Volonteri and Rees2005a; Madau et al. Reference Madau, Rees, Volonteri, Haardt and Oh2004; Volonteri & Rees Reference Volonteri and Rees2006b; Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008; Tanaka & Haiman Reference Tanaka and Haiman2009; Devecchi et al. Reference Devecchi, Volonteri, Colpi and Haardt2010, Reference Devecchi, Volonteri, Rossi, Colpi and Portegies Zwart2012; Petri, Ferrara, & Salvaterra Reference Petri, Ferrara and Salvaterra2012; Dijkstra, Ferrara, & Mesinger Reference Dijkstra, Ferrara and Mesinger2014; Volonteri et al. Reference Volonteri, Silk and Dubus2015).

However, in order to interpret the observed properties of high-redshift quasars discussed in the previous section, it is important to connect all the physical processes regulating the formation of SMBHs and the host galaxies’ chemical evolution history in a self-consistent cosmological framework.

A first attempt to link the chemical evolution of the ISM (metals and dust) to the SMBH formation in z > 6 quasar by means of a pSAM has been made by Valiante et al. (Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014, Reference Valiante, Schneider, Volonteri and Omukai2016) and Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016) employing the cosmological data-constrained model GAMETE/QSOdust. The model successfully reproduces the observed properties of a sample of z > 5 quasars such as the mass of molecular gas, metals, dust, and BHs (Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014), and has been recently improved to investigate different SMBHs formation scenarios. The relative role of low-mass and high-mass seeds is investigated in Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016), while Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016) study the effect of different gas accretion modes/regimes by including new, physically motivated, prescriptions for gas cooling, disk, and bulge formation in progenitor galaxies.

These models are targeted to highly biased regions of the Universe, where an SMBH is expected to form (e.g. Stiavelli et al. Reference Stiavelli2005b; Kim et al. Reference Kim2009b; Utsumi et al. Reference Utsumi, Goto, Kashikawa, Miyazaki, Komiyama, Furusawa and Overzier2010; Morselli et al. Reference Morselli2014), namely single DM halos of 1012–1013 M, which represent the highest density fluctuations at z ~ 6 (e.g. Fan et al. Reference Fan2004; Volonteri & Rees Reference Volonteri and Rees2006b). In other words, all the halos in the merger trees of high-z pSAMs are the ancestors of a single quasar host. In particular, the observed/inferred properties of the best (observationally) studied quasar, J1148 at z = 6.4, are often adopted as a reference data set to constrain/explore model parameters (e.g. Dwek et al. Reference Dwek, Galliano and Jones2007; Valiante et al. Reference Valiante, Schneider, Bianchi and Andersen2009, Reference Valiante, Schneider, Salvadori and Bianchi2011; Dwek & Cherchneff Reference Dwek and Cherchneff2011; Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016; Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016) in the above mentioned studies.

The importance of several physical processes has emerged from both pSAMs and hSAMs, such as metal enrichment of the medium from galactic winds (Dijkstra et al. Reference Dijkstra, Ferrara and Mesinger2014; Habouzit et al. Reference Habouzit2016b) and the clustering radiation sources (Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008; Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012). The dependence of these physical aspects on the spatial halo distribution is better described by hSAMs as cosmological simulations: either DM only or hydrodynamical, directly provide the spatial distribution of halos. In general, hSAMs are designed to describe average volumes of the Universe that are able to probe smaller scales, exploring in detail the environmental conditions required for the formation of the high-redshift BH population.

The population of SDSS quasars presents an observational limit of 1 cGpc−3 for 109 M BHs (e.g. Fan et al. Reference Fan, Strauss, Richards, Hennawi, Becker, White and Diamond-Stanic2006; Venemans et al. Reference Venemans2013; De Rosa et al. Reference De Rosa2014). Much larger volumes, and thus large scale N-body simulations are required to produce one such billion solar mass BH in a statistically significant manner, from either a Pop III or a DCBH seed. On the other hand, small scale N-body simulations (i.e. much smaller volumes ~ 100 cMpc−3) are instead best suited for studying the environment in which the first stars and seed BHs form. Either way, hSAMs operating on either of these volumes present complementary insights into the problem of forming BHs at z > 6.

So far, hSAMs have mostly been used to study the formation of high-mass seeds. For example, Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) and Habouzit et al. (Reference Habouzit2016b) use hSAMs in which DM only simulations permit one to account for effects that are critical to the first galaxy formation paradigm. Local feedback mechanisms such as the net radiation flux and metal pollution can be folded into the construct of hSAMs, along with other recipes such as self-consistent star formation and tracking halo histories across cosmic time.

The first part of this review is dedicated to the description of the environmental conditions required for the formation of different populations of seed BHs in both average volumes, simulated by hSAMs, and biased regions described in pSAMs. We then will briefly discuss different pathways for the fast growth of these seeds up to >109 M BHs at z ~ 6, as well as their co-evolution with the host galaxies.

4 THE FIRST SEED BHs: HOW, WHERE, AND WHEN

In the following sections, we discuss the environmental conditions that enable and regulate the formation of the first seed BHs in a cosmological context, as explored by both pSAMs and hSAMs. We focus our attention on the formation of low-mass (Pop III remnants) and high-mass (DCBHs) seeds.

4.1. Seeds formation sites

As they are the end products of massive Pop III stars, low-mass seed formation is enabled by nearly primordial conditions: metal and dust poor gas fragmenting into one or few massive stars at redshift z ~ 20 (e.g. Abel et al. Reference Abel, Bryan and Norman2002; Heger et al. Reference Heger, Fryer, Woosley, Langer and Hartmann2003; Madau & Rees Reference Madau and Rees2001; Yoshida et al. Reference Yoshida, Omukai and Hernquist2008; Hosokawa et al. Reference Hosokawa, Omukai, Yoshida and Yorke2011; Latif et al. Reference Latif, Schleicher, Schmidt and Niemeyer2013b; Hirano et al. Reference Hirano, Hosokawa, Yoshida, Umeda, Omukai, Chiaki and Yorke2014, Reference Hirano, Hosokawa, Yoshida, Omukai and Yorke2015). Gas enriched up to metallicity Z cr ⩾ 10−4~Z, or dust-to-gas ratios $\cal {D}$ >4 × 10−9, fragments more efficiently (thanks to metal lines cooling and dust continuum radiation), to form instead lower mass, population II (Pop II) stars (Schneider et al. Reference Schneider, Ferrara, Natarajan and Omukai2002, Reference Schneider, Ferrara, Salvaterra, Omukai and Bromm2003; Omukai et al. Reference Omukai, Tsuribe, Schneider and Ferrara2005; Schneider et al. Reference Schneider, Omukai, Bianchi and Valiante2012). Such conditions are expected to be easily met in the first virialised structures at early times, the so-called minihalos, characterise by virial temperatures of 1.2 × 103 < T vir < 104 K and masses M h ~ 105 − 6 M (see e.g. Bromm Reference Bromm2013 for a review).

Although early studies suggest that Pop III star formation in these halos is characterised by high-mass stars (⩾100 M, e.g. Abel et al. Reference Abel, Bryan and Norman2002; Bromm, Coppi, & Larson Reference Bromm, Coppi and Larson2002; Bromm & Loeb Reference Bromm and Loeb2004; Yoshida et al. Reference Yoshida, Omukai and Hernquist2008), more recent simulations have shown that Pop III stars forming under different minihalo environmental conditions (e.g. determined by the presence or absence of photo-dissociating and ionising feedback) may span a wider range of masses, from few tens up to ~1 000 M (e.g. Hirano et al. Reference Hirano, Hosokawa, Yoshida, Umeda, Omukai, Chiaki and Yorke2014, Reference Hirano, Hosokawa, Yoshida, Omukai and Yorke2015; Hosokawa et al. Reference Hosokawa, Hirano, Kuiper, Yorke, Omukai and Yoshida2016). In these works, only one star per halo is formed. However, a number of studies, resolving protostellar scales ( ~ 100R), show that fragmentation of protostellar disks may lead to the formation of multiple stars, with a wide mass spectrum (down to few solar masses), in small clusters (e.g. Clark, Glover, & Klessen Reference Clark, Glover and Klessen2008, Reference Clark, Glover, Smith, Greif, Klessen and Bromm2011; Turk, Abel, & O’Shea Reference Turk, Abel and O’Shea2009; Stacy, Greif, & Bromm Reference Stacy, Greif and Bromm2010, Reference Stacy, Bromm and Lee2016, Greif et al. Reference Greif, Springel, White, Glover, Clark, Smith, Klessen and Bromm2011, Reference Greif, Bromm, Clark, Glover, Smith, Klessen, Yoshida and Springel2012; Susa, Hasegawa, & Tominaga Reference Susa, Hasegawa and Tominaga2014).

Pop III stars also represent the first sources of light and heavy elements (including dust, e.g. Nozawa et al. Reference Nozawa, Kozasa, Habe, Dwek, Umeda, Tominaga, Maeda and Nomoto2007; Heger & Woosley Reference Heger and Woosley2010; Marassi et al. Reference Marassi, Schneider, Limongi, Chieffi, Bocchio and Bianchi2015), setting the stage for all subsequent structure formation in their neighbourhood. Therefore, it is imperative that their formation is captured in the models for a consistent identification of the seed BH hosts. Resolving minihalos, in which these stars form, is thus crucial for models, at least at z > 20. Unfortunately, the mass/size resolution limit in both hSAMs (i.e. the box size and DM particle mass) and the pSAMs (i.e. the minimum DM halo mass) is often determined by the inherent computational costs.

Depending on the aim of the model, different scale/mass resolutions are suited for different studies. Resolving arbitrarily small halos is computationally prohibitive even for analytic binary Monte Carlo algorithms. In pSAMs, the resolution of the merger tree is thus defined by the minimum halo mass, which, together with the adaptive redshift interval (Δz) are chosen to maintain manageable computational times, simultaneously matching the EPS predictions at different redshifts (e.g. Volonteri et al. Reference Volonteri, Haardt and Madau2003; Tanaka & Haiman Reference Tanaka and Haiman2009).

In N-body simulations, the need to resolve a minihalo sets an upper limit on the box size that can be simulated in a reasonable time frame. N-body simulations with volumes ~ 100 cMpc3 allow one to resolve minihalos, capturing the small-scale sub-grid physics. These simulations offer insights on the formation sites of the first stars and seed BHs but lack statistical significance in terms of SMBH abundance for which larger volumes are required as discussed in section 3.

The formation of a DCBH requires the absence of star formation and of efficient coolants (metals and dust) in order to maintain isothermal collapse of gas clouds in Lyman-α-cooling halos (Lyα, T vir ~ 104 K), leading to a Jeans halo mass (which scales as T 3/2) which is high enough to avoid fragmentation. Thus, high-mass seed BHs are expected to form out of poorly enriched gas (Z < Z cr) if star formation is somehow inhibited. Colliding cold accretion flows (e.g. Inayoshi & Omukai Reference Inayoshi and Omukai2012) or high relative velocity galaxy mergers ( ⩾ 200 km s−1) can shock heat the gas in the dense central regions of galaxies, collisionally dissociating the H2 molecules (e.g. Inayoshi, Visbal, & Kashiyama Reference Inayoshi, Visbal and Kashiyama2015), thus preventing the gas from forming stars. Alternatively, the presence of H2 photo-dissociating flux, i.e. photons in the Lyman Werner (LW) band (11.2–13.6 eV) emitted by nearby external sources, may suppress star formation in Lyα cooling halos (e.g. Bromm & Loeb Reference Bromm and Loeb2003; Begelman et al. Reference Begelman, Volonteri and Rees2006; Spaans & Silk Reference Spaans and Silk2006; Inayoshi et al. Reference Inayoshi, Omukai and Tasker2014; Ferrara et al. Reference Ferrara, Salvadori, Yue and Schleicher2014). These conditions indeed enable the formation of a supermassive star (SMS) of 104 − 5 M that may eventually lead to a massive seed BH by accreting the surrounding material (e.g. Bromm & Loeb Reference Bromm and Loeb2003; Begelman et al. Reference Begelman, Volonteri and Rees2006; Lodato & Natarajan Reference Lodato and Natarajan2006, Reference Lodato and Natarajan2007; Inayoshi & Omukai Reference Inayoshi and Omukai2012; Inayoshi et al. Reference Inayoshi, Omukai and Tasker2014; Ferrara et al. Reference Ferrara, Salvadori, Yue and Schleicher2014; Hosokawa et al. Reference Hosokawa, Yorke, Inayoshi, Omukai and Yoshida2013; Sakurai et al. Reference Sakurai, Hosokawa, Yoshida and Yorke2015; Umeda et al. Reference Umeda, Hosokawa, Omukai and Yoshida2016; Woods et al. Reference Woods, Heger, Whalen, Haemmerle and Klessen2017; Haemmerlé et al. Reference Haemmerlé, Woods, Klessen, Heger and Whalen2017). Another pathway to create massive BHs in the presence of an external LW radiation field is via a quasi-star system. A massive star rapidly forms a 10–100 M BH embedded in a radiation pressure supported dense gas cloud which then experiences high gas infall (and therefore accretion) rates ~1 M yr−1, eventually resulting in a more massive 104–5 M DCBH seed (Spaans & Silk Reference Spaans and Silk2006; Begelman, Rossi & Armitage Reference Begelman, Rossi and Armitage2008; Volonteri & Begelman Reference Volonteri and Begelman2010). This peculiarity of the environmental conditions, and the frequency of their occurrence is still under debate (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012, Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014; Habouzit et al. Reference Habouzit2016b; Dijkstra et al. Reference Dijkstra, Ferrara and Mesinger2014; Yue et al. Reference Yue, Ferrara, Salvaterra, Xu and Chen2014; Chon et al. Reference Chon, Hirano, Hosokawa and Yoshida2016). The conditions are sensitive to galaxies’ assembly histories and on the interplay between the effect of chemical, radiative, and mechanical feedback, driven by star formation and BH growth itself.

4.2. Forming the first stars

In star-forming halos, both Pop III or Pop II stars form depending on the chemical enrichment (metallicity) of the gas. Pop III stars form out of metal-free/poor gas (Z < Z cr) while metal/dust-rich gas clouds instead lead to Pop II star formation.

The metallicity of a galaxy is usually the result of the interplay between in-situ and external metal pollution, i.e. stellar nucleosynthetic products injected in the galaxy ISM, and in-falling metal-rich (and dusty) gas ejected from nearby galaxies via supernovae (SNe) and AGN-driven winds.

Most hSAMs allow Pop III stars to form in metal-free halos, i.e. the ones that have never hosted a star in their past and/or pass the critical mass threshold (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012). The mass threshold can be understood as a negative feedback effect of LW photons that delay Pop III SF by a fraction of dissociating H2 molecules in a minihalo. While exposed to LW radiation, J LW,Footnote 2 the halo must grow (or accrete more gas) in order to replenish the H2 content, thereby becoming suitable for Pop III SF (e.g. Machacek, Bryan, & Abel Reference Machacek, Bryan and Abel2001; Wise & Abel Reference Wise and Abel2007; O’Shea & Norman Reference O’Shea and Norman2008). We show this M critJ LW curve expressed as Equation (1) (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012), in Figure 2 (from O’Shea & Norman Reference O’Shea and Norman2008) where

(1) $$\begin{eqnarray} M_{\rm crit} \approx 4 \left(1.25 \times 10^5 + 8.7 \times 10^5 \left({ 4 \pi J_{\rm LW} } \right)^{0.47}\right). \ \end{eqnarray}$$

Figure 2. The M critJ LW relation from O’Shea & Norman (Reference O’Shea and Norman2008), Figure 3. The squares represent their updated calculations while the Machacek et al. (Reference Machacek, Bryan and Abel2001) relation is depicted by the dashed line. The empty square represent the case with J LW = 0. If the mass of a pristine minihalo exposed to a given J LW, lies above the curve formed by the squares, it is considered Pop III star forming.

In their recent pSAMs, Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) and de Bennassuti et al. (Reference de Bennassuti, Salvadori, Schneider, Valiante and Omukai2017) compute the fraction of gas that can cool down and form stars in minihalos as a function of halo virial temperature, redshift, gas metallicity, and level of LW flux J LW at which the halo is exposed. At a given redshift, the halo mass threshold increases with J LW. Progressively more massive minihalos are expected to form stars at lower redshifts, at a fixed J LW. A value J LW ⩽ 0.1 is already high enough to suppress star formation in the less massive minihalos (<(3–4) × 106 M) at z > 20. In good agreement with the gas collapse simulations of O’Shea & Norman (Reference O’Shea and Norman2008), Pop III star formation is inhibited in ⩽107 M pristine (Z = 0) minihalos exposed to an LW flux J LW ⩾ 1, at redshift z < 17. Stronger J LW levels (e.g. >10) sterilise all pristine minihalos already at redshift z = 20.Footnote 3

To date, observations do not provide strong enough constraints on the Pop III IMF. On the other hand, theoretical studies provide predictions on the mass distribution of these stars, that varies among different study (see e.g. the reviews by Bromm Reference Bromm2013; Glover Reference Glover, Wiklind, Mobasher and Bromm2013).

The most commonly adopted scenario in hSAMs (e.g. Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012, Reference Agarwal, Davis, Khochfar, Natarajan and Dunlop2013; Chon et al. Reference Chon, Hirano, Hosokawa and Yoshida2016) is to form one Pop III star in a minihalo, randomly picked from a top-heavy IMF that ranges from 100to1 000 M. For atomic cooling pristine halos, where molecular hydrogen is still present in the central region, generally a cluster of 10–100 Pop III stars are allowed to form (e.g. Greif & Bromm Reference Greif and Bromm2006; Greif et al. Reference Greif, Springel, White, Glover, Clark, Smith, Klessen and Bromm2011, Reference Greif, Bromm, Clark, Glover, Smith, Klessen, Yoshida and Springel2012; Clark et al. Reference Clark, Glover, Smith, Greif, Klessen and Bromm2011), following the same IMF.

Regardless of the DM halo mass, massive Pop III stars with an average mass of ~100–200 M are allowed to form in high-z pSAMs (e.g. Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014; Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016). The number of stars depends on the total stellar mass formed in each star formation episode, and thus on the star formation efficiency and available gas mass. An alternative scenario for Pop III star formation in pSAMs has been proposed by Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016): Pop III stars form with an intrinsic top-heavy IMF in the mass range [10–300] M. Then, this IMF is stochastically sampled, on the fly, according to the time-dependent total mass of newly formed stars. We will discuss the effect of these two different assumptions for Pop III stars formation on the low-mass seed BHs distribution later.

In metal-rich halos, Pop II star formation is generally accounted for by converting a fixed fraction of the available gas into stars. The time/redshift evolution of the gas content is modelled either by scaling the DM halo mass with the universal baryon fraction (e.g. Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008, Reference Dijkstra, Ferrara and Mesinger2014; Habouzit et al. Reference Habouzit2016b) or solving a set of differential equations (e.g. Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014, Reference Valiante, Schneider, Volonteri and Omukai2016; Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012; Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016). In hSAMs, the star formation recipes are usually calibrated to reproduce the cosmic star-formation rate density (CSFRD) observed at z > 6 (Hopkins Reference Hopkins2004; Mannucci et al. Reference Mannucci, Buttery, Maiolino, Marconi and Pozzetti2007; Bouwens et al. Reference Bouwens, Illingworth, Franx and Ford2008; Laporte et al. Reference Laporte2012). Since pSAMs are generally targeted to explain the existence of a single quasar, the models are designed to match the observables of the quasar in question.

4.3. Conditions for direct collapse

The treatment of the DC scenario is now taking advantage of hybrid models where instead of Press–Schechter merger trees, one uses a fully cosmological N-body simulation as a playground for the various recipes critical to DCBH formation. One of the main advantages of using hSAMs to study the formation of SMBHs at early times is the spatial information that enables one to study the dependence of various processes on the halos’ physical distribution within the simulated volume. Nearby star-forming halos emit LW photons that are able to photo-dissociate H2 (Omukai Reference Omukai2001; Omukai et al. Reference Omukai, Schneider and Haiman2008; Shang et al. Reference Shang, Bryan and Haiman2010; Latif et al. Reference Latif, Schleicher, Schmidt and Niemeyer2013a), and thus the spatial distance between halos is a crucial ingredient as it controls the strength of the irradiation flux (e.g. Agarwal et al. Reference Agarwal, Smith, Glover, Natarajan and Khochfar2016a). Anisotropies (fluctuations) in the LW background , due to source clustering and/or proximity to the DCBH host candidate, are indeed the key of the radiation-driven DCBH formation scenario (e.g. Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008, Reference Dijkstra, Ferrara and Mesinger2014; Sugimura, Omukai, & Inoue Reference Sugimura, Omukai and Inoue2014; Agarwal et al. Reference Agarwal, Smith, Glover, Natarajan and Khochfar2016a; Regan, Johansson, & Wise Reference Regan, Johansson and Wise2016, Reference Regan, Johansson and Wise2014). When a proto-galaxy is located nearby an emitting source, spatial correlation makes the difference. Far from the emitting source, the LW photons flux seen by the target halo is too low to affect the fraction of molecular gas which remains high. On the hand, the halo is photo-evaporated, by ionising radiation, if it is too close to the illuminating source (e.g. Regan et al. Reference Regan, Johansson and Wise2016). Time synchronisation matters too. The time elapsed between the starburst onset in the primary halo and the gas collapse in the companions must be short in order to avoid halo photo-evaporation or pollution by heavy elements (e.g. Visbal, Haiman, & Bryan Reference Visbal, Haiman and Bryan2014; Regan et al. Reference Regan, Visbal, Wise, Haiman, Johansson and Bryan2017; Agarwal et al. Reference Agarwal, Regan, Klessen, Downes and Zackrisson2017b).

We provide here an overview of the large-scale feasibility of the DC model, i.e. we do not consider studies related to the formation of individual DCBHs (see e.g. Latif & Ferrara Reference Latif and Ferrara2016 for a review), and rather discuss studies which aim at deriving statistical properties, such as the number density of DCBH sites that form in the early Universe and the conditions leading to them.

In order to identify a DCBH formation site within an average volume of the Universe, one must account for the entire LW and metal pollution history of the atomic cooling halo in question, especially taking into consideration the effects of the local environment. This is one of the biggest strengths of hSAMs as painting galaxies on N-body simulations allows us to compute spatial locations.

4.3.1. Critical LW flux

We have discussed above how (low level) LW flux can delay Pop III star formation in pristine minihalos. Once the halo becomes atomic cooling, i.e. when it attains a virital temperature of T vir > 104 K and the primary coolant becomes atomic hydrogen (Omukai Reference Omukai2000), an extremely high level of flux can completely shut down H2 cooling by dissociating these molecules in the most dense (thus efficiently self-shielded) regions (Omukai Reference Omukai2001; Omukai et al. Reference Omukai, Schneider and Haiman2008; Shang et al. Reference Shang, Bryan and Haiman2010; Latif et al. Reference Latif, Schleicher, Schmidt and Niemeyer2013a).

The critical level J cr, above which DC of gas clouds into massive seeds is enabled, is still a matter of debate and remains a free parameter for models. Assuming that Pop III stellar populations mimic a T = 105 K and Pop II stellar populations a T = 104 K blackbody, Omukai (Reference Omukai2000) computed the critical value of J crit using their 1D spherically symmetric gas collapse model. Since the shape of the blackbody spectrum depends on its temperature, J crit depends on the type of the stellar population externally irradiating the pristine atomic cooling halo. They found J III cr ≈ 104–105 and J II cr ≈ 102–103 is needed from Pop III and Pop II populations to cause DCBH formation in a neighbouring pristine atomic cooling halo. Revisions in this estimate followed by employing high-resolution 3D hydrodynamical simulations and better recipes for H2 self-shielding, leading to an estimate of J III cr ~ 1 000 and J II crit ≈ 10 − 100 (Shang et al. Reference Shang, Bryan and Haiman2010; Wolcott-Green, Haiman, & Bryan Reference Wolcott-Green, Haiman and Bryan2011; Latif et al. Reference Latif, Bovino, Van Borm, Grassi, Schleicher and Spaans2014; Hartwig et al. Reference Hartwig, Glover, Klessen, Latif and Volonteri2015).

In addition, ionising photons and X-rays can both increase the free electron fraction promoting H2 formation (Inayoshi & Omukai Reference Inayoshi and Omukai2011; Yue et al. Reference Yue, Ferrara, Salvaterra, Xu and Chen2014; Johnson et al. Reference Johnson, Whalen, Agarwal, Paardekooper and Khochfar2014; Aykutalp et al. Reference Aykutalp, Wise, Spaans and Meijerink2014; Inayoshi & Tanaka Reference Inayoshi and Tanaka2015). As a result, a higher critical LW level, up to J cr ~ 104–105, is required (Latif et al. Reference Latif, Bovino, Van Borm, Grassi, Schleicher and Spaans2014; Regan et al. Reference Regan, Johansson and Wise2014; Latif & Volonteri Reference Latif and Volonteri2015).

Besides H2 molecules, H- ions play a critical role in pristine gas collapse as they regulate H2 formation at densities n ≲ 103 cm−3 via the reactions

(2) $$\begin{eqnarray} {\rm H} +\ {\rm e} \rightarrow {\rm H}^{-} + \gamma , \end{eqnarray}$$
(3) $$\begin{eqnarray} {\rm H}^{-} +\ {\rm H} \rightarrow {\rm H}_{2} + e^{-}. \end{eqnarray}$$

The importance of this network is further understood by their corresponding photo-destruction channels

(4) $$\begin{eqnarray} {\rm H}_{2} +\ \gamma _{\rm LW} \rightarrow {\rm H} + H, \end{eqnarray}$$
(5) $$\begin{eqnarray} {\rm H}^{-} +\ \gamma _{0.76} \rightarrow {\rm H} + e^{-}, \end{eqnarray}$$

where γLW and γ0.76 represent the photons in the LW band and photons with energy greater than 0.76 eV, respectively. Ignoring the role of 1 eV photons can lead to a gross overestimation in the value of LW flux required to suppress H2 cooling, as demonstrated by Wolcott-Green et al. (Reference Wolcott-Green, Haiman and Bryan2011) and Haiman (Reference Haiman2012). Furthermore, Glover (Reference Glover2015a, Reference Glover2015b) showed that inconsistencies in the chemical networks and reaction rate coefficients can lead to a factor ~3 difference in the determination of J cr.

The assumption of representing Pop III and Pop II SEDs as blackbodies was questioned by Sugimura et al. (Reference Sugimura, Omukai and Inoue2014), Agarwal & Khochfar (Reference Agarwal and Khochfar2015), and Agarwal et al. (Reference Agarwal, Smith, Glover, Natarajan and Khochfar2016a), who showed that using realistic SEDs to represent stellar populations instead drastically alters the paradigm. This is because the change in the slope of a SED with the age of a stellar population alters the rate of production of LW photons (e.g. Schaerer Reference Schaerer2002) with respect to 1 eV photons. Agarwal et al. (Reference Agarwal, Smith, Glover, Natarajan and Khochfar2016a) and Wolcott-Green, Haiman, & Bryan (Reference Wolcott-Green, Haiman and Bryan2017) demonstrated that indeed, one cannot expect a single value of J cr from a given stellar population, but that it is a value dependent on the underlying stellar population’s SFH and varies from 0.1–1 000 in their 1D models. Needless to say, given that these studies are very recent, this variation in the nature of J cr needs to be further explored.

In Figure 3, we show the global and spatial LW intensities from the Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) hybrid fiducial model, and compare them to other studies. The averaged background LW intensity,J bg, at a given redshift is computed as a function of the stellar mass density at that redshift.

$$\begin{equation*} J_{\rm bg}(z) = \frac{hc}{4\pi m_{\rm H}}\eta _{LW}\rho _\star (1+z)^3\ , \nonumber \end{equation*}$$

where ηLW is the number of LW photons emitted per stellar baryon, and ρ is the stellar mass density at a given redshift, z. Both quantities are linked to the stellar population, so that J bg = J III bg + J II bg (see Greif & Bromm Reference Greif and Bromm2006 and Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012 for more details). The green dotted line is instead the specific intensity J bg given by Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014). The yellow dotted line in Figure 3 shows the average LW emission computed in the pSAM of (Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016) (similar values are also shown by Petri et al. (Reference Petri, Ferrara and Salvaterra2012)).

Figure 3. From Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012): The background and local level of LW radiation plotted for each redshift. ‘The red triangles (J II local) and blue crosses (J III local) indicate the maximum value of LW radiation to which a pristine halo is exposed at each redshift in their volume. The red and blue dashed lines represent J II crit and J III crit respectively. It is interesting to see that the maximum value of J III local (blue crosses) falls short of J III crit (blue dashed line). However, in the case of Pop II sources, the maximum value of J II local (red triangles) is several orders of magnitude higher than the J II crit (red dashed line). The green dotted line is the specific intensity J bg given by Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014). Finally, the yellow dotted line shows the average LW emission from Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016).

As it can be seen from the figure, the global LW background radiation, J bg is always far below the critical value for DC, J cr (horizontal dashed red and blue lines). Thus, the study of the spatial variation of the photo-dissociating emission is fundamental to identify potential DCBH formation sites.

Ahn et al. (Reference Ahn, Shapiro, Iliev, Mellema and Pen2009) presented the first study of the evolution of the inhomogeneous LW background, in which the local LW flux intensity is self-consistently computed in a cosmological N-body simulation, explaining its importance. Their study is based on a suite of runs that were originally aimed at understanding reionisation (Iliev et al. Reference Iliev, Mellema, Shapiro and Pen2007), but was modified to include a radiative-transfer module for LW photons. Ahn et al. (Reference Ahn, Shapiro, Iliev, Mellema and Pen2009) find that the average intensity of the LW radiation exceedes the threshold value for H2-cooling and star-formation suppression in minihalos well before the reionisation process is complete. In their scenario, both the average and local LW flux can be ⩾10−2 already at z < 20 (see e.g. Ahn et al. Reference Ahn, Shapiro, Iliev, Mellema and Pen2009: Figure 10). As a result, Lyα-cooling halos are the dominant sources of reionsation while minihalos are sterilised before they can significantly contribute to the ionising and LW background radiation. Following this study, several other models (pSAMs and hSAMs) pointed out the importance of LW flux fluctuations due to sources clustering in the formation of DCBHs (e.g. Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008, Reference Dijkstra, Ferrara and Mesinger2014; Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012; Habouzit et al. Reference Habouzit2016b; Chon et al. Reference Chon, Hirano, Hosokawa and Yoshida2016; Pawlik, Bromm, & Milosavljević Reference Pawlik, Bromm and Milosavljević2014).

In Figure 3, we also show the values of the local LW flux, J local, from single stellar populations as computed by Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) in their hSAM volume at each redshift. They show that while Pop III stars are never able to produce the J III crit in their vicinity, Pop II stars are able to produce J II crit quite easily (see Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012 for details). This result was later confirmed by Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) and Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) in their suite of hydrodynamical runs, and by Chon et al. (Reference Chon, Hirano, Hosokawa and Yoshida2016).

Due to the lack of spatial information, pSAMs instead cannot capture the spatial variations of J LW with respect to the background flux as hSAMs do. However, the LW emission from Pop III/II stars and accreting BHs is self-consistently computed, according to their SED, as a function of stellar age and metallicity and of BH accretion rate (e.g. Petri et al. Reference Petri, Ferrara and Salvaterra2012; Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016). An important difference with respect to hSAMs is that in pSAMs, the star formation and BH accretion efficiency are usually calibrated to match the observed SFR and BH mass of specific, single, obejcts (e.g. quasar J1148 in Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016). Within the biased region occupied by the progenitors of a 1013 DM halo, the computed LW flux can be interpreted as a mean value for the local fluctuations exceeding the background level, as expected by several models (e.g. Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008; Tanaka & Haiman Reference Tanaka and Haiman2009; Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012; Dijkstra et al. Reference Dijkstra, Ferrara and Mesinger2014). In addition, Petri et al. (Reference Petri, Ferrara and Salvaterra2012) and Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) show that stellar emission provides the dominant contribution to the photo-dissociating flux with respect to accreting BHs. For example, the global LW emission from stellar populations in Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016), taken as a proxy of the local flux in their biased region (orange dotted line Figure 3), is in good agreement with the maximum local Pop II LW flux, at z < 11 (red triangles), and with the large scatter in the maximum local Pop III flux, at larger redshifts (blue crosses), from Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012).

4.3.2. The role of metal enrichment

As the first generation of stars form in the Universe, they also create the first wave of metals that provide the conditions for Pop II star formation (e.g. Mackey, Bromm, & Hernquist Reference Mackey, Bromm and Hernquist2003; Greif et al. Reference Greif, Johnson, Bromm and Klessen2007; Whalen et al. Reference Whalen, van Veelen, O’Shea and Norman2008; Joggerst et al. Reference Joggerst, Almgren, Bell, Heger, Whalen and Woosley2010; Ritter et al. Reference Ritter, Safranek-Shrader, Gnat, Milosavljević and Bromm2012). Thus, it is critical to understand metal pollution in terms of both in-situ and external effects. The chemical enrichment of a given halo is indeed the result of the ongoing and past star formation (i.e. metals and dust produced by stars in the parent galaxy and/or its progenitors) as well as contamination by infalling material from outside the halo (galactic winds). Both self-enrichment and winds play a role in setting the conditions for seed BH formation.

As we have seen above, several models (both pSAMs and hSAMs) point out that DCBH regions are expected to be close to star-forming galaxies, in order to maintain a low abundance of H2. These are also the first regions which are exposed to metal pollution from galactic winds driven by SNe and AGN.

Although Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) do not explicitly consider galactic winds in their model, their results on the number and environment of DCBH sites were in good agreement with the FiBY suite of hydrodynamical simulations (Agarwal et al. Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) that did include external metal pollution. This suggests that, for the assumed J cr = 30, the DCBH population is not significantly affected by winds. Using their analytic approach, Agarwal et al. (Reference Agarwal, Regan, Klessen, Downes and Zackrisson2017b) find that even with instantaneous metal mixing, the metal outflows (e.g. due to SN winds) from the irradiating galaxy are unable to prevent the advent of isothermal collapse in the neighbouring DCBH halo. The external atomic cooling site has sufficient time to undergo isothermal collapse in the presence of the LW radiation field before being polluted to Z > Z cr.

Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) explore the effect of metal pollution by both SN-driven galactic outflows and genetic enrichment on the DCBH formation probability by computing the size of regions that can be enriched with metals transported by galactic SN-driven winds and the probability that a halo remains metal free (i.e. it do not inherit metals from its progenitor halos). They show that external metal pollution sterilises DCBH host candidates on a scale of ⩽10 kpc. The results suffer from the lack of spatial information in their pSAM.

The effect of galactic winds has been recently confirmed by Habouzit et al. (Reference Habouzit2016b). In their model, DC is enabled in the vicinity of ~1011 M star-forming halos, that can provide a high enough radiation intensity (J LW > J cr = 100, see Habouzit et al. Reference Habouzit2016b: Figure 3) to halos at a distance of ~15–20 kpc at z > 15, without polluting them. In other words, when the expanding metal-rich bubbles created by SN explosions are still smaller than the regions irradiated by a strong intensity.

By means of a set of differential equations, Valiante et al. (Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014, Reference Valiante, Schneider, Volonteri and Omukai2016) self-consistently follow the global life cycle of the mass of metals and dust in the ISM of J1148 progenitor galaxies taking into account the metal pollution (infalls) of the external medium due to both SN- and AGN-driven winds. They find that a more efficient self-enrichment of galaxies within a merger tree, with respect to the average genetic pollution history, may prevent the formation of DCBHs progenitors before the LW flux exceeds the critical threshold, while infalling metals are responsible for the super-critical enrichment of newly virialised halos (see e.g. Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016).

It is worth noting that, metal mixing is an extremely complicated topic. The time scale for metals escaping their host halo and mixing with the gas of the halo being polluted is not fully understood (e.g. Cen & Riquelme Reference Cen and Riquelme2008; Wise & Abel Reference Wise and Abel2008; Smith et al. Reference Smith, Wise, O’Shea, Norman and Khochfar2015; Chen et al. Reference Chen, Whalen, Wollenberg, Glover and Klessen2016). Additionally, the escape of metals from their parent halo depends on the wind-escape-velocity and the potential well of the halo (Muratov et al. Reference Muratov, Keres, Faucher-Giguere, Hopkins, Quataert and Murray2015; Smith et al. Reference Smith, Wise, O’Shea, Norman and Khochfar2015).

Metal enrichment is indeed predicted to be very disparate in the early Universe, but some halos could remain metal free down to z ~ 6 (Tornatore et al. Reference Tornatore, Borgani, Dolag and Matteucci2007; Fumagalli et al. Reference Fumagalli, O’Meara and Prochaska2011; Pallottini et al. Reference Pallottini, Ferrara, Gallerani, Salvadori and D’Odorico2014). The fraction of metal-free halos, or at least halos below the critical metallicity to avoid fragmentation, depends on chemical and mechanical processes (Schneider et al. Reference Schneider, Salvaterra, Ferrara and Ciardi2006a, Reference Schneider, Omukai, Inoue and Ferrara2006b). Detailed prescriptions of the effects of inhomogeneous enrichment as well as of the physical properties of metal winds escaping from star-forming halos cannot be easily modelled in either pure or hybrid SAMs. However, cosmological hydrodynamical simulations can self-consistently track the evolution of metal enrichment over the entire simulated volumes (Latif et al. Reference Latif, Omukai, Habouzit, Schleicher and Volonteri2016; Agarwal et al. Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014; Habouzit et al. Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c).

Summarising, the combined effect of chemical and radiative feedback sets, the condition for the formation of both low-mass and high-mass seeds as it regulates Pop III/II star formation in all halos and determines the fraction of atomic cooling (Lyα) halos that can potentially host DCBHs at later times. As long as the build up of a super-critical J LW precedes the efficient metal pollution, DCBH formation can occur in atomic cooling halos.

4.4. DCBHs number density

Over the past few years, the question of the number density of DCBHs has become a topic of great interest, and has led to values that span several orders of magnitude, from ~10−1 to 10−9 cMpc−3.

Here we compare the results of both hSAMs (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012; Habouzit et al. Reference Habouzit2016b) and pSAMs of Dijkstra et al. (Reference Dijkstra, Haiman, Mesinger and Wyithe2008, Reference Dijkstra, Ferrara and Mesinger2014) and Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016). We include DCBH number densities from the Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) and Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) hydrodynamical simulations as they offer a direct comparison of semi-analytic and hydrodynamic approaches.

Dijkstra et al. (Reference Dijkstra, Haiman, Mesinger and Wyithe2008) compute the probability distribution function of the LW flux at which DM halos are exposed to at z ~ 10 taking into account their clustering properties. They find that only a small fraction, <10−6, of all atomic cooling halos are exposed to a LW flux exceeding the assumed critical threshold, J LW > 103 and thus derive a number density of <10−6 cMpc−3 potential DCBHs hosts.

In contrast, using a semi-analytic model on top of a cosmological N-body simulation, Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) find a higher number density, in the range 10−2–10−1 cMpc−3 for J crit = 30 (their fiducial model), even accounting for in-situ metal pollution from previous star-formation events.

In their fiducial model, Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) include star formation in atomic cooling halos (but do not include minihalos), metal pollution from progenitor halos, and galactic outflows and estimate n DCBH ~ 10−9–10−6 cMpc−3 between z = 20 and 7. They explore the dependence of their predictions on model assumptions, such as the value of LW photons escape fraction and critical flux for DC, underlying the important effect of galactic winds decreasing the number density by several orders of magnitudes. The fraction of LW photons escaping from galaxies, and contributing to the photo-dissociating background radiation, indeed plays a crucial role in this scenario. However, the LW escape fraction is still highly uncertain (may increases from 0 to 1 depending on halo and stellar mass) and strongly dependent on the ionisation front propagation (e.g. Kitayama et al. Reference Kitayama, Yoshida, Susa and Umemura2004; Schauer et al. Reference Schauer, Whalen, Glover and Klessen2015, Reference Schauer2017b).

More recently, Habouzit et al. (Reference Habouzit2016b) find a number density of DCBH regions in the range 10−7–5 × 10−6 cMpc−3, consistent with what found by Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014). A factor of two higher number density can be found in cosmological N-body simulations in which primordial fluctuations are described by a non-Gaussian distribution. In addition, they also estimate the Pop III remnant BHs number density, being about two order of magnitude higher than that of DCBHs, although they do not resolve minihalos in their simulations. Similar values are found in hydrodynamical simulations by Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) for different box sizes and resolutions.

In their pSAM aimed to study the role of Pop III remnant BHs and DCBHs in the formation of a z ~ 6 SMBH, Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) predict an average number density of ~10−7 cMpc−3 DCBHs. These are the DCBHs expected to form in J1148 progenitor galaxies, along the hierarchical history of a 1013 M DM halo. As we will discuss later, only a fraction of these high-mass seeds eventually end in the final SMBH, driving its fast growth.

In Figure 4, we show a collection of DCBH number densities derived from some of the studies discussed above. Symbols represent different radiation intensity thresholds: squares refer to J LW, crit = 30, circles to J LW, crit = 100, and triangles to J LW, crit = 300. The figure is taken from Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) who compare the results of semi-analytic studies by Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) (dark gray symbols) with hydrodynamical simulations: one of the FiBy simulations based on the smoothed particle hydrodynamics (SPH) code gadget (e.g. Springel et al. Reference Springel, Di Matteo and Hernquist2005a) presented by Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) (light grey crossed square at z = 10.5); two runs of the 10 cMpc box Chunky simulation with a collapse times scale equal to 10 Myr (purple symbols) and to the halo free fall time, t ff (orange square); the large-scale (142 cMpc side box) cosmological simulation Horizon-noAGN (cyan symbols, Dubois et al. Reference Dubois2014b; Peirani et al. Reference Peirani2016). We refer the reader to the original paper Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) for a detailed discussion. We have included in this figure the predictions by Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) in the z = 7 − 10 redshift range (light gray squares) and Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) at z = 18 and 15 (black triangles). Finally, the horizontal blue solid line show the SMBH number density observed at z ~ 6 of 1 cGpc−3.

Figure 4. Co-moving number density of halos that can host a DCBH, at a given redshift. Symbols represent different radiation intensity thresholds. Squares: J LW, crit = 30, circles: J LW, crit = 100, triangles: J LW, crit = 300. The horizontal solid blue line shows the co-moving number density of z ~ 6 SMBHs. The light gray crossed square at z = 10.5 is from the hydrodynamical simulation by Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014), the light gray squares in the range z = 10–7 are from Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012, private communication), dark gray squares and black triangles are the results of Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) and Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016), respectively. The orange square shows the number density for Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) (10 cMpc side box, t ff, see text). The purple squares and circles show the number density for Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) (10 cMpc side box, 10 Myr, see text). The cyan squares, circle, and triangle represent the large-scale cosmological simulation Horizon-noAGN (Dubois et al. Reference Dubois2014b; Habouzit et al. Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c, 142 cMpc side box).

4.4.1. Consensus between different studies

One of the most restrictive ingredient of the DC scenario is the absence of H2 (through both H2 destruction and prevention of H2 formation) to keep the gas temperature and thus the Jeans mass high enough to avoid the fragmentation of gas clouds. This should favour the formation of only one massive object. As mentioned in section 2, the exposure to a strong LW radiation is one of the possible way to strongly depress H2 abundances (Omukai Reference Omukai2000; Omukai et al. Reference Omukai, Schneider and Haiman2008; Shang et al. Reference Shang, Bryan and Haiman2010). From Ahn et al. (Reference Ahn, Shapiro, Iliev, Mellema and Pen2009), we have understood that the spatial variations of the radiation intensity, driven by LW photons able to photo-dissociate H2, was certainly a key requirement of the scenario. Most of the models for the radiation intensity include now a spatial varying component based on local photo-dissociating sources. The radiation intensity is either computed directly from stellar particles according to their age, distance, and redshift (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012, Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014; Habouzit et al. Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c), or from the stellar mass painted on DM halos (Dijkstra et al. Reference Dijkstra, Ferrara and Mesinger2014; Habouzit et al. Reference Habouzit2016b; Chon et al. Reference Chon, Hirano, Hosokawa and Yoshida2016).

Moreover, the critical radiation flux needed to destroy H2, seems to be driven mainly by Pop II stars. This is supported by three main ideas. First of all, the LW radiation background created by Pop III stars emission, impacts their surrounding by photo-dissociating molecular hydrogen. Cooling rate decreases, which delays the gas collapse, and this vicious circle lowers and delays the formation of new Pop III stars at later time (O’Shea & Norman Reference O’Shea and Norman2008; Johnson et al. Reference Johnson, Whalen, Fryer and Li2012). The life time of Pop III stars is also thought to be short (~10 Myr), it could be too short for providing a high LW radiation intensity during the whole free-fall time of a DM halo. One can compute the redshift at which the free-fall time is approximately equal to ~10 Myr, and finds z ~ 45. This means that a halo illuminated only by Pop III radiation, could form a BH only at very early times, around z ~ 45. Finally, the intensity of Pop III radiation itself may be not enough to provide the critical radiation intensity commonly assumed for the DC model (O’Shea & Norman Reference O’Shea and Norman2008; Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012). In Figure 5 (reproduced from Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012), we show the distribution of the local varying radiation intensity seen by pristine halos at z = 16, before the formation of the first Pop II stars, and z ~ 9, after their formation. Radiation intensity from Pop III stars is shown in blue, and from Pop II stars in red. Dashed lines indicate the critical radiation intensity expected for Pop III stars (in blue) and Pop II stars (in red). Pop III stars radiation intensity appears to be almost always below the critical intensity (below the corresponding red dashed line), whereas a majority of pristine halos under Pop II stars radiation flux can meet the critical radiation intensity condition. The distribution of radiation intensity to which halos are exposed to, is in good agreement between various studies, using similar methods and LW radiation modellings (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012; Chon et al. Reference Chon, Hirano, Hosokawa and Yoshida2016), or different approaches (Dijkstra et al. Reference Dijkstra, Haiman, Mesinger and Wyithe2008).

Figure 5. Distribution of local radiation intensity (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) seen by pristine halos at z = 16 (top panel), before the formation of Pop II begins, and later on at z ~ 9 (bottom panel) when Pop II is already in place. f pris is the number fraction of pristine halos exposed to a given J LW. Radiation intensity from Pop III stars is shown in red, and radiation intensity from Pop II stars in blue. Dashed lines indicate the critical radiation intensity expected for Pop III stars (in red) and Pop II stars (in blue). Pop III stars radiation intensity appears to be almost always below the critical intensity (below the corresponding dashed line), whereas a fraction of pristine halo illuminated by Pop II stars radiation flux can meet the critical radiation intensity condition.

Finally, all studies agree that metal pollution from both heritages, previous episodes of star formation in halo progenitors and galactic winds from nearby halos, could play a fundamental role. Galactic winds could sterilise potential DCBH regions by enriching them in metals, on a scale of ⩽ 10 kpc, thereby reducing their number density (Dijkstra et al. Reference Dijkstra, Ferrara and Mesinger2014). The process is a complex interplay of metals mixing in a gas medium of varying density, the propagation of metals in the IGM, and the winds launching out from their host halo (Cen & Riquelme Reference Cen and Riquelme2008; Smith et al. Reference Smith, Wise, O’Shea, Norman and Khochfar2015). Agarwal et al. (Reference Agarwal, Regan, Klessen, Downes and Zackrisson2017b) recently devised a semi-analytical model working under worst case assumptions for DCBH formation under the influence of metals originating from neighbouring galaxies that provide the necessary LW flux. Even after assuming an extremely short (300 pc) separation between their DCBH candidate halo and external LW sources, and instantaneous metal mixing, they find that the metal mixing is insufficient to shut down DCBH formation. This is because during the time window when the target halo can form a DCBH, its metallicity remains well below the critical threshold above which SF is expected (Omukai et al. Reference Omukai, Schneider and Haiman2008; Latif et al. Reference Latif, Omukai, Habouzit, Schleicher and Volonteri2016).

4.4.2. Why do we have a spread in the number density

The large diversity of models (modelling of the photo-dissociating radiation intensity, and metal-enrichment, for example), methods (pSAMS, hybrid with DM only simulations, or hydrodynamical ones), set-up of simulations (star formation, SN feedback), used to estimate the number density of DCBH regions, complicate the task of comparing their results. Despite the fact that all the studies presented here seem to agree pretty well, several of the models use different assumptions. In this section, we identify the main differences between the different models.

Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) perform a comparison between the SAM model of Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) and the hybrid model of Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014), and find that compared to hydrodynamical simulations, Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) overestimate the stellar mass that form in halos. In the opposite, Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) underestimate the number of galaxies that contribute to radiation, and the extent of metal-polluted bubbles (the latter can vary strongly depending on the stellar mass going SN, and the medium properties). In some cases, the different assumptions compensate each other, and lead to the same estimate of the number density of the potential DCBH host halos (Habouzit et al. Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c).

Differences between models using DM only simulations and models from hydrodynamical simulations can be studied by comparing Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) (distribution of halos from a DM simulation) and Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) (hydrodynamical simulation). The number density derived by Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) is shown in light gray squares in Figure 4, whereas the number density from Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) is represented in crossed square point in Figure 4. Agarwal et al. (Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014) is an improvement of Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012), because now, thanks to the hydrodynamical output, the model takes into account self-consistently cooling of halos, metal enrichment through SN feedback, molecular dissociation, and photo-ionisation.

As discussed above, hSAMs are largely adopted to study the feasibility of the DCBH formation scenario. However, one would eventually want to know whether these high-mass seed BHs, that formed at early times, can actually grow and form the population of quasar we see at z = 6, and under which conditions this is possible (accretion, galaxy–galaxy mergers, super-Eddington episode, and so on).

Most of the studies discussed in this review provide upper limits on the number density of DCBHs, because they are not able to follow all the physical processes from the selection of DM halos to the collapse of the gas to form a BH. However, they seem to all show that the DCBH number density is higher than the observed number density of quasars at high redshift, 10−9 cMpc−3, horizontal blue line in Figure 4 (Fan et al. Reference Fan, Strauss, Richards, Hennawi, Becker, White and Diamond-Stanic2006; Mortlock et al. Reference Mortlock2011). If a higher critcal flux is required for DCBH formation (J crit > 100), as it is actually found in 3D zoom-in simulations, then Dijkstra et al. (Reference Dijkstra, Ferrara and Mesinger2014) (see also Habouzit et al. Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c, with the large-scale simulation Horizon-noAGN) show that the upper limit on the DCBH number density is sufficient to reproduce the population of quasars. However, such high critical values do not explain the population of less massive BHs that we observe today in more normal and low-mass galaxies (Greene Reference Greene2012; Reines, Greene, & Geha Reference Reines, Greene and Geha2013).

On the other hand, smaller simulation boxes that resolve minihalos and include a more developed chemistry network, have lead to the derivation of higher DCBH region number density, particularly because they impose a lower critical radiation intensity (J crit = 30) (Agarwal et al. Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012, Reference Agarwal, Dalla Vecchia, Johnson, Khochfar and Paardekooper2014). Such low values of the critical intensity could suggest that the DC scenario may also be able to seed the more normal galaxies. Recently, Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c) show that this also strongly depends on SN feedback implementation, and that to explain BHs in normal galaxies, a weak SN feedback is required.

Although large progress has been made, both in terms of pure SAMs and hybrid models to investigate the DC scenario, owing to the large spread in the number density of DCBH regions derived, and the uncertainty in the nature of the critical LW radiation intensity, it is still unclear if the DC scenario can produce enough BHs to explain the population of high-redshift quasars.

Regarding the target of this review, high-redshift quasars, a natural follow-up of these studies would be to follow the growth of the BHs, modelling the accretion and feedback as a function of host halo merger history. To this aim, a number of semi-analytic studies have been developed so far (see section 3). In the following part of the manuscript, we will review state-of-the-art results on the growth of z ~ 6 SMBHs and their host galaxies.

5 FROM SEEDS TO THE FIRST QUASARS

Several studies have investigated the early growth of SMBHs starting from either low-mass or high-mass seeds (see reviews by e.g. Volonteri et al. Reference Volonteri2010; Natarajan Reference Natarajan2011; Volonteri & Bellovary Reference Volonteri and Bellovary2012; Alexander & Hickox Reference Alexander and Hickox2012; Haiman Reference Haiman, Wiklind, Mobasher and Bromm2013; Johnson & Haardt Reference Johnson and Haardt2016). In these models, SMBHs growth is driven by both gas accretion and mergers with other BHs. In this section, we briefly review the most recent studies of the hierarchical assembly of a quasar and its host galaxy, as described by pSAMs.

5.1. Low-mass vs. high-mass seeds

In Figure 6, we show the distribution of the number of seed BHs formed along the hierarchical build-up of a z ~ 6 quasar (i.e. in its progenitor galaxies) as a function of the host DM halo mass. In the left panel, we show the number of equal mass stellar BHs, low-mass seeds of 102 M, assumed to form in newly virialised halos, as long as they are metal poor, Z < Z cr = 10−3.8, i.e. at z ⩾ 20, as predicted by Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016). The other two panels instead are for a mixed-seed-based seeding prescription (Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016): (40–140) and (260–300) M Pop III remnant BHs (middle panel) plus 105 M high-mass seeds (right panel), forming along the same merger history. In this scenario, the formation of low-mass and high-mass seeds is simultaneously explored thus, allowing to directly compare the role of the two channels in the formation of an SMBH. In all panels, histograms and data points are obtained by averaging over 29(10) different merger histories of the 1013 M DM halo in the low-mass-seed(mixed-seed) case, with error bars showing the 1σ dispersion. Both prescriptions have been adopted to model the quasar, J1148 at z = 6.4, with a SMBH of (2–6) × 109 M (Barth et al. Reference Barth, Martini, Nelson and Ho2003; Willott, McLure, & Jarvis Reference Willott, McLure and Jarvis2003; De Rosa et al. Reference De Rosa, Decarli, Walter, Fan, Jiang, Kurk, Pasquali and Rix2011). As we discussed in the previous sections, the number, redshift, and typical host halo mass of both low-mass and high-mass seeds is determined by the interplay between the early chemical enrichment—due to metal-rich infalling gas from the external medium, polluted by SN- and AGN-driven winds from other galaxies—and the intensity of the LW radiation (from both stars and accreting BHs) to which the halos are exposed.

Figure 6. Distribution of the average number of seed BHs as function of the DM halo mass from different seeding prescriptions adopted in pSAMs: (i) equal-mass 100 M low-mass seeds (left panel) and (ii) (10–140) and (260–300) M Pop III remnant BHs (middle panel) plus 105 M high-mass seeds (right panel). Histograms and data points show the number of total (in lighter colours) and real SMBH progenitors (darker histograms, see text). Error bars account for the 1σ dispersion. The figures are adapted from Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016) and Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016). The average redshift range in which seeds form, according to these two models, is given in each panel.

The inclusion of radiative feedback effects results in a less efficient and slightly slower metal enrichment, enabling Pop III stars to form on average down to lower redshift, e.g. z ~ 16 in the model shown on Figure 6. As we see in the right panel of the figure, DCBH form in 107–108 M progenitor halos [and in the narrow redshift range 15–18, see Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) for details], consistent with what is expected from their formation theory and the findings of Bellovary et al. (Reference Bellovary, Volonteri, Governato, Shen, Quinn and Wadsley2011), Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012), Habouzit, Volonteri, & Dubois (Reference Habouzit, Volonteri and Dubois2016a), and Chon et al. (Reference Chon, Hirano, Hosokawa and Yoshida2016).

In their pSAM, Petri et al. (Reference Petri, Ferrara and Salvaterra2012) combine both low-mass and high-mass seeds to investigate their relative role in the formation of SMBHs in a pSAM. They explore the dependence of the resulting SMBH evolutionary scenario on the fraction of halos (exposed to an LW flux J cr > 103) that can actually host DCBHs. A 109–1010 M BH is formed at z ~ 6 if at least (1–10)% of all the halos host a high-mass seed (see Petri et al. Reference Petri, Ferrara and Salvaterra2012: Figures 4 and 9).

For a critical LW threshold J cr > 300 (Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016) predict an average high-mass seeds occurrence ratio (the number of galaxies with Z < Z cr when J LW > J cr divided by the number of all the halos exposed to a flux J LW > J cr) of ~5% at z > 15. This suggests that chemical feedback plays a dominant role in determining of the birth environment.Footnote 4

Recently, Schauer et al. (Reference Schauer, Regan, Glover and Klessen2017a) explore the effects of baryonic streaming velocities on minihlaos, offering an alternative pathway to inhibit Pop III star formation before the pristine halo reaches the atomic cooling limit. Chon et al. (Reference Chon, Hirano, Hosokawa and Yoshida2016) combined a semi-analytic model for galaxy formation with halo merger trees extracted from N-body DM simulations to select possible DCBH hosts among atomic cooling halos. By means of zoom-in cosmological hydrodynamical simulations of the selected halos, they explore the evolution of gas collapse in the DCBH sites. They mostly follow the approach of Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) but bring a previously unexplored effect to light: tidal gravitational fields affecting gas collapse. They show that unless assembled via major mergers, their DCBH sites do not survive the tidal fields and get disrupted before an isothermal collapse can ensue at gas densities of n ⩾ 10 cm−3. A DCBH occupation fraction of ~5% (2 out of the selected 42) is found in this study, in good agreement with the pSAM of Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016).

5.2. The role of mergers and BH dynamics

Merger events can serve as an important physical process that drives the growth of BHs. However, binary (or multiple) BH interactions, driven by dynamical friction, are quite complex, multi-scale processes. The physical scales of interest span from sub-pc scales of the Schwarzschild radius (e.g. ~10−11 pc for 100–300 M BHs and ~10−8–10−7 pc for BHs of 105–106 M) up to the Mpc scale of the host galaxy mergers. In addition, the mechanism leading to BH–BH mergers, the time it takes for BHs to coalesce via gravitational wave (GW) emission, and the relation between the end state of the merger and the properties of the respective host galaxies, are still open questions.

However, SAMs aimed to study the formation and evolution of SMBHs through cosmic time usually adopt simple prescriptions to account for the contribution of mergers to the BH growth (see e.g. Tanaka & Haiman Reference Tanaka and Haiman2009 and references therein).

In major mergers,Footnote 5 BHs follow the fate of their host galaxies, coalescing to form a more massive BH. However, during this process, a large centre-of-mass recoil (kick) can be imparted to the newly formed BH as a consequence of asymmetric GW emission (e.g. Campanelli et al. Reference Campanelli, Lousto, Zlochower and Merritt2007; Schnittman et al. Reference Schnittman, Buonanno, van Meter, Baker, Boggs, Centrella, Kelly and McWilliams2008; Baker et al. Reference Baker, Boggs, Centrella, Kelly, McWilliams, Miller and van Meter2008). The acquired kick velocity can be as large as ~ 100 kms−1, enough to eject the coalesced binary out of the host galaxy (see e.g. Yoo & Miralda-Escudé Reference Yoo and Miralda-Escudé2004; Volonteri & Rees Reference Volonteri and Rees2006b; Tanaka & Haiman Reference Tanaka and Haiman2009; Barausse Reference Barausse2012 and references therein for details). On the other hand, in minor mergers, one of the two merging BHs, usually the least massive one, is assumed to remain as a satellite in low-density regions, without accreting or contributing to the growth of the final BH.

The effective number of seed BHs from which an SMBH forms depends on these assumptions. Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) predict that only ~13% of the low-mass and high-mass seeds in their model (darker histograms in middle and right panels in Figure 6) contribute to the final mass of the SMBH of J1148, at z = 6.4, as a large fraction of BHs is lost due to minor mergers.

A similar fraction, ~15% (indicated by the darker histogram in the left panel) is left by taking into account the combined effect of minor mergers and gravitational recoil on growing low-mass seeds. On average, ~56% satellites BHs are lost along the entire merger tree in minor mergers while ~32% of the coalescing BHs, in major merger events, gain a recoil velocity large enough to exceed the retention speed, being kicked out of the galaxies (Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016; a much larger fraction, ~99% is found by Volonteri et al. Reference Volonteri, Haardt and Madau2003).

The effect of BH recoil due to GW emission during BH mergers has been also studied by Sijacki, Springel, & Haehnelt (Reference Sijacki, Springel and Haehnelt2009). They resimulate the most massive z = 6 DM halo extracted from the Millennium simulation in order to study the effect of BH mergers (Blecha et al. Reference Blecha2016) in the growth of high-redshift massive BHs. An SMBH of 109–1010 M is produced in an Eddington-limited scenario, by planting massive BH seeds of 105 M, in DM halos with masses 109–10 M at z = 15. They find that if the initial BH spin is high, the growth of mostly isolated (only a small number of mergers occur) massive BHs is hampered. However, BH kicks substantially expel low-mass BHs, and thus do not affect the overall growth of the SMBHs.

BH mergers are found to play a minor role in the formation of the first SMBHs (at relatively lower redshifts), in pSAMs (e.g. Figure 6 in Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016) and recently in hydrodynamical simulations like MassiveBlack and BlueTides (e.g. Feng et al. Reference Feng, Di Matteo, Croft and Khandai2014a and Di Matteo et al. Reference Di Matteo, Croft, Feng, Waters and Wilkins2016).

Mergers between BHs drive the BH mass assembly only at high redshifts (but see Petri et al. Reference Petri, Ferrara and Salvaterra2012). For example, although driving the BH growth process at z > 11, BH–BH coalescences contributes to less than 1% of the J1148 final BH mass at z = 6.4 (Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011). Similarly, in Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016), BH mergers (of mainly low-mass seeds) are predicted to drive the BH growth down to z ~ 15, before the gas accretion regime triggered by the formation of the first high-mass seeds, sets in.

Conversely, in the large-volume, cosmological hydrodynamical simulation Horizon-AGN (box size of 100 h −1 Mpc and resolution mass of 8 × 107 M), Dubois et al. (Reference Dubois, Volonteri and Silk2014a) show an accretion-dominated BH growth at high redshift, while in the older Universe, the galactic centres tend to be less gas-rich, and, thus, the mass growth of the central BHs is mostly driven by mergers. In addition, a demographic study of BHs has been recently carried out by Volonteri et al. (Reference Volonteri, Dubois, Pichon and Devriendt2016b) within the same simulation. They show that the fraction of BH host galaxies is higher at higher stellar masses and that multiple BHs are hosted in the most massive halos as a consequence of merger events. A population of dual AGN, a central, and an off-centre accreting BH, is found in the simulated halos.

Recent ALMA observations presented by Trakhtenbrot et al. (Reference Trakhtenbrot, Lira, Netzer, Cicone, Maiolino and Shemmer2017a) have revealed a large number, ~50%, of massive star-forming galaxies interacting with quasar hosts (within <50 kpc scales). The authors argue that this may support the idea of major merger-driven growth playing an important role in the formation of SMBHs in high-redshift quasars, at least those showing sub-mm galaxy (SMG) companions. The z ~ 5 quasar in the sample shows similar properties in terms of BH mass and bolometric luminosity but varies in terms of host galaxy properties (see Netzer et al. Reference Netzer, Mor, Trakhtenbrot, Shemmer and Lira2014 and Trakhtenbrot et al. Reference Trakhtenbrot, Lira, Netzer, Cicone, Maiolino and Shemmer2017a for details on the sample), suggesting different accretion mechanisms may be operating in different environments.

5.3. The role of gas accretion

Semi-analytic techniques have been largely employed to study the role of different gas accretion regimes and/or the effect of dynamical processes in the early growth of SMBHs (e.g. Volonteri et al. Reference Volonteri, Haardt and Madau2003, Reference Volonteri and Rees2005a; Begelman et al. Reference Begelman, Volonteri and Rees2006; Volonteri & Rees Reference Volonteri and Rees2005b, Reference Volonteri and Rees2006b; Tanaka & Haiman Reference Tanaka and Haiman2009; Volonteri et al. Reference Volonteri, Silk and Dubus2015).

Volonteri & Rees (Reference Volonteri and Rees2006b) show that the observed high-z SMBH masses can be reproduced starting from low-mass seeds (~100 M) if they accrete gas at super-Eddington rates, at early stages. Super-Eddington accretion is a selective and biased process, occurring only for a small fraction of BH seeds if they form in metal-free atomic cooling (T vir ⩾ 104K) halos (e.g. Volonteri & Rees, Reference Volonteri and Rees2005b, Reference Volonteri and Rees2006b).

Gas accretion rates that are 104 times higher than the Eddington rate can be reached by low-mass seeds in super-Eddington models (e.g. Volonteri & Rees Reference Volonteri and Rees2005b, Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016, and references therein). However, mildly super-Eddington intermittent accretion at ${\sim }3-4 \dot{M}_{\rm Edd}$ (or in general, $<20 \dot{M}_{\rm Edd}$ ) may be efficient enough to grow an SMBH in less than 800 Myr (at z ~ 7) starting from a single (e.g. Madau et al. Reference Madau, Haardt and Dotti2014: Figure 2) or a population (e.g. Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016: Figure 5) of 100 M BH seeds.

In Figure 7, we show the plot presented by Volonteri & Rees (Reference Volonteri and Rees2006b) to illustrate the SMBH mass growth along the merger tree of a 1013 M halo at z = 6. The figure depicts the effect of different accretion regimes and/or radiative efficiencies on the mass assembly of a ~100 M seed that starts accreting at z = 24: Eddington-limited with a radiative efficiency ε = 0.1, 0.2, 0.4 (solid, short-dashed, and dot-dashed lines, respectively) and super-Eddington (long-dashed line). Radiatively efficient gas accretion disks (ε > 0.1) strongly limit the growth of their BH, even while accreting continuously at the Eddington rate.

Figure 7. The growth of a low-mass seed BH mass as a function of redshift in different regimes: Eddington-limited gas accretion with radiative efficiencies ε = 0.1, 0.2, 0.4 (solid, short-dashed and dot-dashed lines, respectively); super-critical accretion (long-dashed line). The figure is taken from Volonteri & Rees (Reference Volonteri and Rees2006b).

The requirement for episodic, radiatively inefficient, super-critical gas accretion onto stellar mass seed of 20–100 M is supported by sub-pc resolution hydrodynamical simulations presented by Lupi et al. (Reference Lupi, Haardt, Dotti, Fiacconi, Mayer and Madau2016). They compare two different methods, the Adaptive Mesh Refinement technique used in the code ramses, and the Lagrangian Godunov-type method adopted in gizmo. In addition, 3D radiation magnetohydrodynamic simulations suggest that super-Eddington accretion flows can drive the rapid growth of low-mass BHs simultaneously, enabling high levels of both radiative and mechanical feedback (Jiang, Stone, & Davis Reference Jiang, Stone and Davis2014). On the other hand, super-critical accretion onto low-mass BH seeds is not supported by radiation hydrodynamic models for BH formation in Hii regions, which instead suggest rather low rates of accretion, below (or at most close to) the Eddington limit (e.g. Milosavljević, Couch, & Bromm Reference Milosavljević, Couch and Bromm2009a; Milosavljević et al. Reference Milosavljević, Bromm, Couch and Oh2009b; Park & Ricotti Reference Park and Ricotti2011, Reference Park and Ricotti2012, Reference Park and Ricotti2013).

Very recently, the analysis of a sample of 20 quasars, including ULAS J1120 and SDSS J0100 at z ⩾ 5.8 presented by Trakhtenbrot, Volonteri, & Natarajan (Reference Trakhtenbrot, Volonteri and Natarajan2017b) suggest that the inferred BH masses and luminosities can be naturally explained by means of a classical thin accretion disk model, with radiative efficiencies in the range [0.04–0.4] and sub-Eddington accretion rates. This support the idea that super-critical growth may have occurred at earlier cosmic epochs (z > 10, e.g. Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016).

Super-Eddington gas accretion regime is not only adopted for low-mass seeds growth. In their recent analytic model, Volonteri et al. (Reference Volonteri, Silk and Dubus2015) show that galactic inflow rates as high as 1–100 M yr−1 may trigger a sequence of fast (104–107 yr) episodes of super-critical accretion, onto both low-mass or high-mass seeds, at rates which are 102–104 times larger than in the Eddington-limited scenario (see Volonteri et al. Reference Volonteri, Silk and Dubus2015: Figure 2). As a result of these intermittent phases of short super-Eddington gas accretion, an SMBH can be produced.

In the super-Eddington scenarios, the radiatively inefficient slim disk model (Abramowicz et al. Reference Abramowicz, Czerny, Lasota and Szuszkiewicz1988) ensures that even in the presence of hyper-Eddington accretion ( ${>}>20 \dot{M}_{\rm Edd}$ ), the bolometric luminosity of the accreting BH is only mildly super-Eddington, L bol/L Edd ⩽ (2–4) (e.g. Mineshige et al. Reference Mineshige, Kawaguchi, Takeuchi and Hayashida2000; Volonteri & Rees Reference Volonteri and Rees2006b; Madau et al. Reference Madau, Haardt and Dotti2014; Volonteri et al. Reference Volonteri, Silk and Dubus2015; Pezzulli et al. Reference Pezzulli, Valiante and Schneider2016).

In Eddington-limited gas accretion scenarios, in which the BH can accrete at most at the Eddington rate, the formation of high-mass seeds, enabled by the LW radiative feedback is crucial to explain the fast growth of z ~ 6 SMBHs (see e.g. Johnson et al. Reference Johnson, Whalen, Li and Holz2013, the recent pSAMs of Petri et al. Reference Petri, Ferrara and Salvaterra2012; Valiante et al. Reference Valiante, Schneider, Volonteri and Omukai2016, and the review by Johnson & Haardt Reference Johnson and Haardt2016). In their mixed-seed-based model, Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) determine the relative contribution of low-mass and high-mass seeds to the final BH mass of J1148. They report that efficient Eddington-limited growth relies on the formation of ≈1–10 high-mass seeds in order to produce the expected SMBH mass at z = 6.4. If high-mass seed formation is prevented, the predicted final BH mass does not exceed ~106 M, thus warranting the need for super-Eddington accretion in the low-mass seeds scenario.

Finally, a new cosmological semi-analytic model for galaxy formation, including the growth of SMBHs within a large box size (1.12 cGpc h −1) N-body simulation (hSAM), has been presented by Makiya et al. (Reference Makiya2016). Their model is currently tuned to reproduce the properties of local galaxies. Using this simulation, Shirakata et al. (Reference Shirakata2016) suggest that stringent constraints on the seed BH mass, may come from less massive bulges observed at z ~ 0, rather than the high-redshift BH-bulge mass relation. Their study suggests that the mass of BHs observed in ~109 M bulges is overpredicted if only seeding by high-mass seeds (105 M) is considered. Such small stellar mass bulges instead favour seeding by smaller seed BHs (103 M) or a mixed population of seed BHs randomly distributed in the mass range 103–105 M.

Numerical simulations of equal-mass protogalaxies encounters show that merger-driven gas inflows are able to trigger the formation (without requiring the suppression of star formation) and rapid growth of a massive BH (Mayer et al. Reference Mayer, Kazantzidis, Escala and Callegari2010) as well as of actively accreting SMBH binaries (Mayer et al. Reference Mayer, Kazantzidis, Madau, Colpi, Quinn and Wadsley2007). Recently, a suite of high spatial resolution simulations (~10 pc) have been devoted to study the effect of galaxy mergers on BH accretion, as a function of the initial merging galaxies’ mass ratio, orbital configuration, and gas fraction. These different stages of galactic encounters is described in Capelo et al. (Reference Capelo, Volonteri, Dotti, Bellovary, Mayer and Governato2015). They confirm that more efficient BH accretion is induced during galaxy mergers with the initial mass ratio being the most critical parameter affecting BH accretion and AGN activity.

In the simulations presented by Feng et al. (Reference Feng, Di Matteo, Croft and Khandai2014a) and Di Matteo et al. (Reference Di Matteo, Croft, Feng, Waters and Wilkins2016), the rapid growth of BHs, occurring in bulge dominated galaxies, is driven by large-scale filamentary cold gas accretion, rather than by major gas-rich mergers. Feng et al. (Reference Feng, Di Matteo, Croft and Khandai2014b) extract three DM halos from the cosmological hydrodynamical simulation MassiveBlack, hosting 109 M BHs and re-simulate them with zoom-in techniques. They find that dense cold gas is able to sustain accretion. During the accretion phase at the Eddington rate, the cold gas directly feeds the BH, while in the sub-Eddington phase (that they find for z ≲ 6), the accretion disc is disturbed and disrupted by feedback. A recent numerical simulation, including X-rays radiation transport, presented by Smidt et al. (Reference Smidt, Whalen, Johnson and Li2017) suggest that both the SMBH observed in ULAS J1120 and SDSS J0100 (at z ~ 7 and z = 6.3, respectively) can form from 105 M BH seeds (planted at z = 19.2) growing via cold accretion streams. The models reproduce the observed properties of the two quasars, such as the host galaxy mass, SFR, metallicity, luminosity, and ionised near zone, including the dynamical mass enclosed within the inner 1.5 kpc region of the ULAS J1120 host galaxy, inferred from recent ALMA observations (Venemans et al. Reference Venemans2017).

Although the numerous studies presented to date, we cannot yet draw firm conclusions on which growth mechanism (via super- or sub-critical accretion disks, cold accretion streams, mergers) and/or seed formation channel (low-mass vs. high-mass seeds) is to be preferred, or more viable than the others, for high-redshift SMBH formation.

5.4. BH feedback

As discussed in Section 2, the physical processes involved in quasar formation and evolution are expected to be regulated by AGN and stellar feedback. During the quasar-dominated regime (z ≲ 8, see Section 6.2), a strong, galaxy-scale wind is predicted to be driven by the energy released during both BH accretion and SN explosions. This feedback is expected to clear the ISM of gas and dust leaving a un-obscured line of sight towards the central emitting source. In addition, radiation emitted from the optically bright quasar J1148 may contribute to at least 30% of the observed FIR luminosity (>20 μm) heating the large amount of dust (~3 × 108 M) in the host galaxy ISM, outside the un-obscured cone. Both stellar and quasar optical/UV emission are expected to be reprocessed by dust, thus contributing to the observed FIR luminosity (Schneider et al. Reference Schneider, Bianchi, Valiante, Risaliti and Salvadori2015).

Adopting an energy-driven wind prescription similar to that usually adopted by numerical simulations (e.g. Di Matteo et al. Reference Di Matteo, Springel and Hernquist2005), pSAMs show that the AGN feedback is the main driver of the massive observed gas outflow rates at z > 6. This is predicted to have a dominant effect with respect to stellar feedback (energy-driven winds from SN explosions) in shaping the high-z BH-host galaxy co-evolutionary path. For example, a powerful quasar-driven gas outflow is launched during the latest stages of the evolution (~100–200 Myr) in the best-fitting models of Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Maiolino, Salvadori and Bianchi2012 and Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016), for J1148. The predicted outflow rates are in good agreement with the observations, >1 000–3 000 M yr−1 (Maiolino et al. Reference Maiolino2012; Cicone et al. Reference Cicone2015) and ~103 times more efficient than the sub-dominant SN-driven contribution.

However, it is worth noting that the prescription usually adopted in SAMs to describe the energy-driven wind effects cannot provide insights on the physical processes determining the observed properties of the outflowing gas and its complex dynamics.

Although described by sub-grid prescriptions, the response of the gas to the energy released by the accreting BH is now well described by hydrodynamical simulations. Costa, Sijacki, & Haehnelt (Reference Costa, Sijacki and Haehnelt2014) study AGN feedback using the moving-mesh code AREPO. They find that, despite the fact that momentum-driven outflows predict an M BH − σ relation similar to that observed, the energy-driven scenario better reproduces the observed, large-scale anisotropic AGN-driven outflows. With the same code, Costa, Sijacki, & Haehnelt (Reference Costa, Sijacki and Haehnelt2015) re-simulate a zoom-in region around the six most massive halos at z ~ 6 to study the brightest quasars. They show that the high-velocity extended cold gas observed out to ~30 kpc (Maiolino et al. Reference Maiolino2012; Cicone et al. Reference Cicone2015) requires the combined effect of SN and AGN feedback. SN-driven winds are responsible for the pre-enrichment of the circumgalactic and intergalactic medium in which the massive, fast ( > 1 400 kms−1) AGN-driven hot outflow is launched, ensuring efficient radiative cooling (see e.g. Figure 2 in Costa et al. Reference Costa, Sijacki and Haehnelt2015) to explain the presence of cold gas (see e.g. Cicone et al. Reference Cicone2015).

Finally, high velocity (102–103 km s−1) energy-driven winds on large scales have been recently also studied by Bieri et al. (Reference Bieri, Dubois, Rosdahl, Wagner, Silk and Mamon2017) by means of radiation-hydrodynamic simulations of isolated galactic discs. They suggest that outflow rates as high as ~103 M yr−1 are sustained by IR radiation, with scattering on dust grains enabling efficient momentum transfer to the gas.

6 THE HOST GALAXY PROPERTIES

6.1. The origin of high-z dust

Several theoretical models have been devoted to the study of the rapid enrichment of the ISM in z > 6 galaxies and quasars, and in particular to the origin of the huge amount of dust (>108 M) inferred from the FIR and sub-mm observations (e.g. Hirashita & Ferrara Reference Hirashita and Ferrara2002; Morgan & Edmunds Reference Morgan and Edmunds2003; Dwek, Galliano, & Jones Reference Dwek, Galliano and Jones2007; Dwek & Cherchneff Reference Dwek and Cherchneff2011; Valiante et al., Reference Valiante, Schneider, Bianchi and Andersen2009, Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014; Gall et al. Reference Gall, Hjorth and Andersen2011a, Reference Gall, Andersen and Hjorth2011b; Mattsson Reference Mattsson2011, Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014; Calura et al. Reference Calura, Gilli, Vignali, Pozzi, Pipino and Matteucci2014).

An SN origin for the dust observed in the early Universe has often been advocated because of the shorter evolutionary time scale of core collapse SNe progenitors (10–40 M stars, with an age <10 Myr) with respect to that of AGB stars (e.g. Morgan & Edmunds Reference Morgan and Edmunds2003; Dwek, Galliano, & Jones Reference Dwek, Galliano and Jones2007). This scenario was supported by the deviation of the dust extinction curves of z > 4 quasars and gamma ray bursts (GRB) from the Small Magellanic Cloud (SMC) extinction curve, typical of z < 2 quasars (Maiolino et al. Reference Maiolino, Schneider, Oliva, Bianchi, Ferrara, Mannucci, Pedani and Sogorb2004; Stratta et al. Reference Stratta, Maiolino, Fiore and D’Elia2007; Perley et al. Reference Perley2010; Gallerani et al. Reference Gallerani2010). This suggests either a different dust production mechanism or dust processing into the ISM at high redshift.

However, subsequent studies pointed out that stellar sources alone cannot account for the entire dust budget and grain growth in cold, dense gas clouds must also have a dominant role, even at z > 6 (e.g. Michałowski et al. Reference Michałowski, Murphy, Hjorth, Watson, Gall and Dunlop2010; Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011; Pipino et al. Reference Pipino, Fan, Matteucci, Calura, Silva, Granato and Maiolino2011; Rowlands et al. Reference Rowlands, Gomez, Dunne, Aragón-Salamanca, Dye, Maddox, da Cunha and van der Werf2014; but see Ferrara et al. Reference Ferrara, Viti and Ceccarelli2016).

Moreover, in contrast to what was previously thought, AGB stars are able to significantly contribute to dust production in high-redshift quasars, producing a dust mass at least similar to that of SNe, already at z ~ 8–10 depending on the host galaxies’ SFH and IMF (see Valiante et al. Reference Valiante, Schneider, Bianchi and Andersen2009 and Figure 8 in Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011).

Figure 8. The cosmic cycle of a typical quasars at z ~ 6. Models reproduce the properties of J1148 (see text). Left panel: the build-up of the M BHM star relation through cosmic time as compared with data and empirical fit for local galaxies (Sani et al. Reference Sani, Marconi, Hunt and Risaliti2011). Middle panel: the predicted star formation history via quiescent and merger-driven bursts (see e.g. Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011). Left panel: the assembly of the dust mass into the ISM as a function of the stellar mass. In all panels, the solid lines show the average over 50 different DM halo merger trees with shades representing the 1σ dispersion. These figures are adapted from Valiante et al. (Reference Valiante, Schneider, Salvadori and Bianchi2011).

Modelling the properties, and in particular the evolution of dust, in quasar host galaxies at z > 6 is still a major challenge. Li et al. (Reference Li2007, Reference Li2008) carried out the first multi-scale simulation, using GADGET2 (Springel et al. Reference Springel, Di Matteo and Hernquist2005a), aimed to follow the formation of quasar J1148 in a hierarchical scenario, accounting for self-regulated BH growth (starting from Pop III seeds), AGN feedback and the host galaxy properties evolution. They showed that the metallicity and dust mass of J1148 are produced through a series of efficient bursts of star formation (see Figure 7 in Li et al. Reference Li2007) resulting in a final stellar mass of 1012 M, similar to what is expected from the local M BHM relation. To date, this is the only attempt to study the high-z dust properties made with numerical approaches (Li et al. Reference Li2008). However, only a single plausible hierarchical build-up of the J1148 DM halo, extracted (and re-simulated) from the 1h −1 Gpc3 volume is explored in these works and thus, the resulting SFH is unique. Semi-analytic models, which instead enable a statistical investigation of different SFHs, provide similar conclusions. The chemical properties of the host galaxy require an order of magnitude higher stellar mass with respect to the dynamical constraint, as discussed in the following sections.

6.2. The BH-host galaxy co-evolution

Observational campaigns at z > 5 show that quasars and their host galaxies are characterised by similar properties in terms of the BH, dynamical, dust, and molecular gas masses, suggesting a common evolutionary scenario.

In Figure 8, we show the best-fit evolutionary scenario for the BH and host galaxy properties of J1148 as predicted by Valiante et al. (Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014). Solid lines show the redshift evolution of the total massesFootnote 6 of BH and stars (on the left), the total SFR (in the middle), and dust and stars again (on the right) averaged over 50 different DM halo merger trees, with shaded areas representing the 1σ error.

As soon as efficient star formation starts, the BH grows in the buried AGN. At this stage, its optical emission is outshined by the ongoing strong star burst, SFRs from 100 up to >1 000 M yr−1, at z ~ 8 (middle panel). The mass of dust (right panel) rapidly grows, reaching values as high as 109 M, when the bulk of the stellar mass, ~(2–4) × 1011 M, is already in place. During this dust-obscured phase, the total nuclear BH mass reaches ~2 × 108 M.

In this scenario, the progenitor galaxies of J1148 at z ~ 8–10 are predicted to have similar properties (e.g. BH, stellar, and dust mass) as the observed SMGs at lower redshifts (e.g. Santini et al. Reference Santini2010; Michałowski et al. Reference Michałowski, Murphy, Hjorth, Watson, Gall and Dunlop2010; Magnelli et al. Reference Magnelli2012). These sub-mm galaxies are suggested to be the evolutionary stage preceding the active quasar phase.

The transition between the starburst-dominated regime and the quasar-dominated evolution, at z < 8, is triggered by powerful energy-driven winds which clear up the ISM of dust and gas (see e.g. the down turn indicated by the black arrow in the right panel of Figure 8), un-obscuring the line of sight towards the quasar and damping the SFR (we will discuss the AGN feedback in the following section).

SMBH evolution models suggest a steeper evolution of the BH-stellar bulge mass relation at high redshift, with the SMBH forming before/faster than the stellar bulge (e.g. Petri et al. Reference Petri, Ferrara and Salvaterra2012). In addition, the observed deviation of high-redshift quasars from the local BH-stellar-mass ratio seems to be a natural outcome of SMBH growth driven by episodic super-Eddington accretion which leads to a BH accretion rate-to-SFR ratio of >102 (Volonteri et al. Reference Volonteri, Silk and Dubus2015).

Agarwal et al. (Reference Agarwal, Davis, Khochfar, Natarajan and Dunlop2013) track the subsequent growth of DCBH seeds by using a modified version of the Agarwal et al. (Reference Agarwal, Khochfar, Johnson, Neistein, Dalla Vecchia and Livio2012) hSAM. In their simulated volume, they find that the merger of a DCBH host satellite with the neighbouring galaxy (source of the LW radiation field), leads to the resultant system lying above the local M BHM star relation, already at these early stages of the evolution. The authors term this phase as ‘obese black hole galaxies’ or OBGs as the DCBH is able to outshine the stellar component, leading to unique observables that distinguish them from normal galaxies. The OBGs are expected to transition onto the local M BHM star relation via mergers. However, they do not account for the formation and evolution of metals and dust in the ISM, which represent a strong constraint on the host galaxy SFH and final stellar mass.

Chemical evolution models instead point out that SFR, gas, metals, and dust content of quasar host galaxies are well reproduced with standard assumptions of stellar IMF, star-formation efficiency, and dust grain growth, for galaxies with stellar masses ⩾1011 M (see left panel of Figure 8). These are about one order of magnitude higher than the stellar masses inferred from the observations of high-redshift quasars (e.g. Wang et al.; Reference Wang2010, Reference Wang2013) and would bring the predicted M BHM star relation closer to the local value, suggesting that high-redshift dynamical (and thus stellar) masses may be underestimated (Valiante et al., Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014; Calura et al. Reference Calura, Gilli, Vignali, Pozzi, Pipino and Matteucci2014).

Although a top-heavy IMF scenario (i.e. biased to more massive stars) can reproduce the observed dust mass and the deviation of J1148 from the local M BHM star relation, it requires a less-efficient SFH to do so. This results in an SFR at z = 6.4 that is more than 10 times smaller than the observed rate (Valiante et al. Reference Valiante, Schneider, Salvadori and Bianchi2011), too small to provide the observed FIR luminosity even if the AGN contribution to dust heating (Schneider et al. Reference Schneider, Bianchi, Valiante, Risaliti and Salvadori2015) is accounted for.

Instead, assuming a short evolutionary time scale does not solve the tension either. At the observed SFR ~1 000 M yr−1, the ~(3–4) × 1010 M stellar mass estimated for quasars like J1148 would be produced in a quite short time interval, ~10–20 Myr. Such an evolutionary time scale is too short for stellar evolution to account for dust enrichment up to >108 M, even with a maximally efficient mode of dust formation by SNe (see Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014 for a detailed discussion).

Following this discussion, it is important to note that, at z > 6, stellar masses cannot be convincingly obtained via SED fitting as in local and lower redshift systems. A lower limit to the stellar mass (dynamical bulge) is usually obtained as M star = M dynM H2 where dynamical and molecular gas masses, M dyn and M H2 , respectively, are derived from CO observations.

Large uncertainties are introduced by the methods adopted to infer M dyn and M H2 . A large scatter (>60%) in the estimated molecular gas mass is due to the adopted CO line luminosity-to-H2 mass conversion factor, αCO = 0.8 ± 0.5 M/(K km s−1 pc2). This is typical of ultra luminous infrared galaxies (ULIRGs, Solomon et al. Reference Solomon, Downes, Radford and Barrett1997; Downes & Solomon Reference Downes and Solomon1998) and usually adopted for high-redshift quasars too. In addition, M dyn strongly depends on geometrical assumptions for gas distribution which is usually considered to be disk-like, with given inclination angle i and radius R, which are difficult to infer from observations at such high redshifts. An uncertainty of more than 50% must be associated to the inferred values, M dynsin2 i = (1010–1011) M. A radius R = 2.5 kpc and an inclination angle i = 65 have been inferred for J1148, in which the CO emitting region is spatially resolved (Walter et al. Reference Walter, Carilli, Bertoldi, Menten, Cox, Lo, Fan and Strauss2004). For other quasars, a similar radius and a mean inclination angle of 40 are usually assumed (see e.g. Maiolino et al. Reference Maiolino2007; Wang et al. Reference Wang2010).

Theoretical studies suggest that lower inclination angle (i < 30) and/or larger disk radius (R ~ 5–30) kpc may solve the so-called stellar mass crisis (see e.g. Figure 9 and discussion in Valiante et al. Reference Valiante, Schneider, Salvadori and Gallerani2014).

Recent Atacama Large Millimeter and sub-mm Array (ALMA) observations of [Cii] emission in quasars have suggested that a large fraction of the CO may be still undetected (Wang et al. Reference Wang2013), supporting the idea that dynamical mass estimates could be missing some of the stars. Moreover, IRAM Plateau de Bure Interferometer (PdBI) follow-up observations of [Cii] 158μm emission line and FIR continuum in J1148 host galaxy have revealed the presence of an extended cold gas component out to ~30 kpc which may be an indication of star-formation activity on larger scales with respect to the size of the CO emission (Cicone et al. Reference Cicone2015).

Thus, stellar mass estimates from model predictions and observations may be reconciled by accounting for a more complex and/or more extended star and gas distribution, beyond the few kpc radius inferred from the CO emitting regions. Observations (Cicone et al. Reference Cicone2015), SAMs (Valiante et al., Reference Valiante, Schneider, Salvadori and Bianchi2011, Reference Valiante, Schneider, Salvadori and Gallerani2014; Calura et al. Reference Calura, Gilli, Vignali, Pozzi, Pipino and Matteucci2014) and numerical simulations (e.g. Khandai et al. Reference Khandai, Feng, DeGraf, Di Matteo and Croft2012) seem to agree with this scenario. Quasars at z ~ 5 resolved in the MassiveBlack simulation are predicted to be compact and gas-rich systems with intense burst of star formation occurring in both the innermost and outer regions, out to the DM halo virial radius (~200h −1 kpc, Khandai et al. Reference Khandai, Feng, DeGraf, Di Matteo and Croft2012).

In addition, Di Matteo et al. (Reference Di Matteo, Croft, Feng, Waters and Wilkins2016) show that the most massive BHs (>108 M) at z ~ 8 reside in compact bulge-dominated galaxies (more than 80% of the stars are in the spheroidal component). The total stellar masses of these systems are already >1010 M (see e.g. Figure 1 and Table 1 of Di Matteo et al. Reference Di Matteo, Croft, Feng, Waters and Wilkins2016), bringing them well within the scatter of the observed local M BHM star relation. Pure SAMs provide very similar results.Footnote 7

Finally, Lyu, Rieke, & Alberts (Reference Lyu, Rieke and Alberts2016) derived a typical stellar mass of (3–5) × 1011 M on the basis of the IR SED analysis of about 100 quasars at z > 5, suggesting a BH-galaxy mass ratio of 10−3–10−2, consistent with local relations.

7 DISCUSSION

In this review, we have discussed the formation of the first quasars, and in particular the rapid growth of their SMBHs focussing on pure semi-analytic or hybrid (SAM plus N-body simulations) approaches.

For comparison, we have also mentioned the results of some of the state-of-the-art hydrodynamical simulations, providing deep insights on the dynamical evolution of galaxies. With respect to these simulations, semi-analytic (pure or hybrid) methods have the complementary role of enabling statistical studies and exploring different models and parameter space, on shorter computational time scales.

However, simplified geometries, models for the gas cycling and/or sub-grid prescriptions limit the scope of both pSAMs and hSAMs. Indeed, some physical aspects are still far from being taken into account in these models, such as the gas physics, feedback from stars, and/or the accreting BH, or accretion rate in the inner part of the halo. This is where cosmological hydrodynamical simulations offer a laboratory to study the impact of physical processes related to the structure of collapsed objects.

Angular momentum, for example, is one such physical process. Gravitational systems, such as halos can possess a given degree of rotational support, which is described by the spin parameter ${\lambda _{\text{spin}}}=J\vert E \vert ^{1/2}/GM_{\rm h}^{5/2}$ , with J the angular momentum of halos, E the total energy, and M h the mass of halos. The angular momentum of a halo, or its baryonic central region, is thought to be the result of clustering/surrounding neighbours applying tidal torques on the given halo (Peebles Reference Peebles1969).

Although they have the advantage of directly tracking the cosmic evolution of the baryonic component of galaxies (where semi-analytic models need to use approximations), the main limitation of hydrodynamical simulations is that the physical processes, acting on different scales cannot be described simultaneously, yet.Footnote 8 In other words, large and small scales cannot be resolved at the same time in simulations. This has been widely discussed by Habouzit et al. (Reference Habouzit, Volonteri, Latif, Dubois and Peirani2016c), in the case of DCBH formation. They use a small scale (1 cMpc), high resolution (M DM, res ~ 2 × 103 M) to study in detail the effect of expanding metal-rich bubbles around possible DC sites, while a larger box size (10 cMpc) with intermediate resolution (M DM, res ~ 107 M) is adopted to statistically access the impact of metal enrichment, SFR and SN-driven winds on the DCBH number density, in a significant volume of the Universe. Finally, the Horizon-noAGN large box (142 cMpc), low-resolution (8 × 107 M) simulation is adopted to test whether DCBHs are able to explain the population of high-redshift quasars.

Among the most recent hydrodynamical simulations devoted to study the rare, high-density peaks DM halo hosting the first quasars, MassiveBlack (Di Matteo et al. Reference Di Matteo, Khandai, DeGraf, Feng, Croft, Lopez and Springel2012) and its high-resolutions zooms (Khandai et al. Reference Khandai, Feng, DeGraf, Di Matteo and Croft2012; Feng et al. Reference Feng, Di Matteo, Croft and Khandai2014a), investigate the formation of SMBHs in the first galaxies, by covering a volume of 0.75 Gpc3. A higher resolution is reached in the 0.5 Gpc3 volume of the BlueTides simulation (Feng et al. Reference Feng, Di Matteo, Croft, Tenneti, Bird, Battaglia and Wilkins2015, Reference Feng, Di-Matteo, Croft, Bird, Battaglia and Wilkins2016), enabling the study of the formation of the first SMBHs at early cosmic epochs (z > 7, Di Matteo et al. Reference Di Matteo, Croft, Feng, Waters and Wilkins2016).

Given the advancement in theoretical modelling techniques, all the different approaches can together be considered as a powerful tool to investigate different physical processes related to the formation and evolution of the first quasars at z ~ 6. Combined with observational constraints from current and future high-resolution instruments, these models can be further improved to provide definitive answers to the open questions discussed in Section 2.

ACKNOWLEDGEMENTS

We thank Raffaella Schneider and Marta Volonteri for triggering this review. RV thanks the EWASS2015 organisers for including the Symposium on Understanding the growth of the first supermassive black holes in their programme. RV and EP thank F. Weng and X. B. Wu for providing useful data. M.H. thanks M. Latif and M. Volonteri for useful discussion. BA would like to acknowledge the funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) via the ERC Advanced Grant STARLIGHT (project number 339177). RV has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 306476 while writing the manuscript.

Footnotes

1 Note that the SFR is usually inferred using the FIR Luminosity-SFR scaling relation (Kennicutt Reference Kennicutt1998) which relies on the assumption that all FIR radiation comes from dust heated by stellar optical-UV emission. A factor of 2–3 lower SFRs are found taking into account that in luminous quasars, like the ones observed at z > 6, 30–60% of the dust heating may be due to the AGN emission itself (Wang et al. Reference Wang2010; Schneider et al. Reference Schneider, Bianchi, Valiante, Risaliti and Salvadori2015). Indeed, Schneider et al. (Reference Schneider, Bianchi, Valiante, Risaliti and Salvadori2015) show that the optically bright quasar J1148 may contribute 30–70% of the observed FIR luminosity ( > 20 μm) heating the large amount of dust (~3 × 108 M) in the host galaxy ISM. We refer the reader to Valiante et al. (Reference Valiante, Schneider, Salvadori and Gallerani2014) and Schneider et al. (Reference Schneider, Bianchi, Valiante, Risaliti and Salvadori2015) for a discussion.

2 Note that we use the term flux and specific intensity interchangeably in the manuscript where both refer to a specific intensity in the LW band in units of 10−21erg−1 s −1 cm −2 Hz −1 sr −1.

3 Note that Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) and de Bennassuti et al. (Reference de Bennassuti, Salvadori, Schneider, Valiante and Omukai2017) also investigate the dependence of the M critJ LW relation on metallicity. They show that the presence of a small amount of metals does not significantly affect the results as long as Z ⩽ 10−1.5Z. At higher metallicities, gas cooling and thus star formation can occur in progressively smaller halos so that ~106 M minihalos are able to form stars already at z ≲ 20 (we refer the readers to the original papers for more details).

4 Indeed, if for example, a factor of ~4 higher J cr is assumed in this model, the formation of high-mass seeds is completely suppressed by chemical feedback.

5 Usually major and minor mergers are defined according to the mass ratio of the two merging DM halos (e.g. Tanaka & Haiman Reference Tanaka and Haiman2009 and reference therein). For example, a mass ratio higher than 1 : 10 is assumed by Volonteri & Rees (Reference Volonteri and Rees2006b) to identify major merger events.

6 At each redshift, the total BH mass is given by the sum of the masses of all the existing nuclear BHs. In the same way, the total stellar and dust masses represent the stellar and dust content summed over all the existing halos. See Valiante et al. (Reference Valiante, Schneider, Salvadori and Bianchi2011) for details.

7 A mean BH and stellar mass of 4 × 108 and 3 − 4 × 1010 M are predicted in both low-mass- and mixed-seeds scenarios presented in Valiante et al. (Reference Valiante, Schneider, Volonteri and Omukai2016) and Pezzulli et al. (Reference Pezzulli, Valiante and Schneider2016).

8 In addition, due to the higher computational costs required to run hydrodynamical simulations, these models are often restricted to few realisations, small volumes, and/or still require sub-grid prescriptions (just like SAMs).

References

REFERENCES

Aalto, S., Garcia-Burillo, S., Muller, S., Winters, J. M., van der Werf, P., Henkel, C., Costagliola, F., & Neri, R. 2012, A&A, 537, A44 10.1051/0004-6361/201117919 2012A&A...537A..44A Google Scholar
Abel, T., Bryan, G. L., & Norman, M. L. 2002, Science, 295, 93 10.1126/science.295.5552.93 2002Sci...295...93A Google Scholar
Abramowicz, M. A., Czerny, B., Lasota, J. P., & Szuszkiewicz, E. 1988, ApJ, 332, 646 10.1086/166683 1988ApJ...332..646A CrossRefGoogle Scholar
Agarwal, B., & Khochfar, S. 2015, MNRAS, 446, 160 10.1093/mnras/stu1973 2015MNRAS.446..160A Google Scholar
Agarwal, B., Khochfar, S., Johnson, J. L., Neistein, E., Dalla Vecchia, C., & Livio, M. 2012, MNRAS, 425, 2854 10.1111/j.1365-2966.2012.21651.x 2012MNRAS.425.2854A Google Scholar
Agarwal, B., Davis, A. J., Khochfar, S., Natarajan, P., & Dunlop, J. S. 2013, MNRAS, 432, 3438 10.1093/mnras/stt696 2013MNRAS.432.3438A Google Scholar
Agarwal, B., Dalla Vecchia, C., Johnson, J. L., Khochfar, S., & Paardekooper, J.-P. 2014, MNRAS, 443, 648 10.1093/mnras/stu1112 2014MNRAS.443..648A Google Scholar
Agarwal, B., Johnson, J. L., Khochfar, S., Pellegrini, E., Rydberg, C.-E., Klessen, R. S., & Oesch, P. 2017a, MNRAS, 469, 231 10.1093/mnras/stx794 2017arXiv170200407A Google Scholar
Agarwal, B., Johnson, J. L., Zackrisson, E., Labbe, I., van den Bosch, F. C., Natarajan, P., & Khochfar, S. 2016b, MNRAS, 460, 4003 10.1093/mnras/stw1173 2016MNRAS.460.4003A Google Scholar
Agarwal, B., Regan, J., Klessen, R. S., Downes, T. P., & Zackrisson, E. 2017b, arXiv:1703.08181Google Scholar
Agarwal, B., Smith, B., Glover, S., Natarajan, P., & Khochfar, S. 2016a, MNRAS, 459, 4209 10.1093/mnras/stw929 2016MNRAS.459.4209A Google Scholar
Ahn, K., Shapiro, P. R., Iliev, I. T., Mellema, G., & Pen, U.-L. 2009, ApJ, 695, 1430 10.1088/0004-637X/695/2/1430 2009ApJ...695.1430A Google Scholar
Alatalo, K., et al. 2011, ApJ, 735, 88 10.1088/0004-637X/735/2/88 2011ApJ...735...88A Google Scholar
Alexander, D. M., & Hickox, R. C. 2012, NewAR, 56, 93 10.1016/j.newar.2011.11.003 2012NewAR..56...93A Google Scholar
Alexander, D. M., Swinbank, A. M., Smail, I., McDermid, R., & Nesvadba, N. P. H. 2010, MNRAS, 402, 2211 10.1111/j.1365-2966.2009.16046.x 2010MNRAS.402.2211A Google Scholar
Alvarez, M. A., Wise, J. H., & Abel, T. 2009, ApJ, 701, L133 10.1088/0004-637X/701/2/L133 2009ApJ...701L.133A Google Scholar
Angulo, R. E., Hahn, O., & Abel, T. 2013, MNRAS, 434, 1756 10.1093/mnras/stt1135 2013MNRAS.434.1756A Google Scholar
Aykutalp, A., Wise, J. H., Spaans, M., & Meijerink, R. 2014, ApJ, 797, 139 10.1088/0004-637X/797/2/139 2014ApJ...797..139A Google Scholar
Baker, J. G., Boggs, W. D., Centrella, J., Kelly, B. J., McWilliams, S. T., Miller, M. C., & van Meter, J. R. 2008, ApJ, 682, L29 10.1086/590927 2008ApJ...682L..29B Google Scholar
Balmaverde, B., et al. 2017, arXiv:1706.023172017arXiv170602317B Google Scholar
Bañados, E., et al. 2016, ApJS, 227, 11 10.3847/0067-0049/227/1/11 2016ApJS..227...11B CrossRefGoogle Scholar
Barausse, E. 2012, MNRAS, 423, 2533 10.1111/j.1365-2966.2012.21057.x 2012MNRAS.423.2533B Google Scholar
Barth, A. J., Martini, P., Nelson, C. H., & Ho, L. C. 2003, ApJ, 594, L95 10.1086/378735 2003ApJ...594L..95B Google Scholar
Becerra, F., Greif, T. H., Springel, V., & Hernquist, L. E. 2015, MNRAS, 446, 2380 10.1093/mnras/stu2284 2015MNRAS.446.2380B Google Scholar
Becker, G. D., Sargent, W. L. W., Rauch, M., & Simcoe, R. A. 2006, ApJ, 640, 69 10.1086/500079 2006ApJ...640...69B CrossRefGoogle Scholar
Beelen, A., Cox, P., Benford, D. J., Dowell, C. D., Kovács, A., Bertoldi, F., Omont, A., & Carilli, C. L. 2006, ApJ, 642, 694 10.1086/500636 2006ApJ...642..694B Google Scholar
Begelman, M. C., Rossi, E. M., & Armitage, P. J. 2008, MNRAS, 387, 1649 Google Scholar
Begelman, M. C., Volonteri, M., & Rees, M. J. 2006, MNRAS, 370, 289 10.1111/j.1365-2966.2006.10467.x 2006MNRAS.370..289B Google Scholar
Bellovary, J., Volonteri, M., Governato, F., Shen, S., Quinn, T., & Wadsley, J. 2011, ApJ, 742, 13 10.1088/0004-637X/742/1/13 2011ApJ...742...13B Google Scholar
Bertoldi, F., Carilli, C. L., Cox, P., Fan, X., Strauss, M. A., Beelen, A., Omont, A., & Zylka, R. 2003, A&A, 406, L55 10.1051/0004-6361:20030710 2003A&A...406L..55B Google Scholar
Bieri, R., Dubois, Y., Rosdahl, J., Wagner, A., Silk, J., & Mamon, G. A. 2017, MNRAS, 464, 1854 10.1093/mnras/stw2380 2017MNRAS.464.1854B Google Scholar
Bieri, R., Dubois, Y., Silk, J., & Mamon, G. A. 2015, ApJ, 812, L36 10.1088/2041-8205/812/2/L36 2015ApJ...812L..36B Google Scholar
Blecha, L., et al. 2016, MNRAS, 456, 961 10.1093/mnras/stv2646 2016MNRAS.456..961B Google Scholar
Bouwens, R. J., Illingworth, G. D., Franx, M., & Ford, H. 2008, ApJ, 686, 230 10.1086/590103 2008ApJ...686..230B Google Scholar
Bowler, R. A. A., McLure, R. J., Dunlop, J. S., McLeod, D. J., Stanway, E. R., Eldridge, J. J., & Jarvis, M. J. 2016, MNRAS, 469, 448 Google Scholar
Bromm, V. 2013, RPPh, 76, 112901 10.1088/0034-4885/76/11/112901 2013RPPh...76k2901B Google Scholar
Bromm, V., Coppi, P. S., & Larson, R. B. 2002, ApJ, 564, 23 10.1086/323947 2002ApJ...564...23B Google Scholar
Bromm, V., & Loeb, A. 2003, ApJ, 596, 34 10.1086/377529 2003ApJ...596...34B Google Scholar
Bromm, V., & Loeb, A. 2004, NA, 9, 353 10.1016/j.newast.2003.12.006 2004NewA....9..353B Google Scholar
Calura, F., Gilli, R., Vignali, C., Pozzi, F., Pipino, A., & Matteucci, F. 2014, MNRAS, 438, 2765 10.1093/mnras/stt2329 2014MNRAS.438.2765C Google Scholar
Campanelli, M., Lousto, C., Zlochower, Y., & Merritt, D. 2007, ApJ, 659, L5 10.1086/516712 2007ApJ...659L...5C Google Scholar
Cano-Díaz, M., Maiolino, R., Marconi, A., Netzer, H., Shemmer, O., & Cresci, G. 2012, A&A, 537, L8 10.1051/0004-6361/201118358 2012A&A...537L...8C Google Scholar
Capelo, P. R., Volonteri, M., Dotti, M., Bellovary, J. M., Mayer, L., & Governato, F. 2015, MNRAS, 447, 2123 10.1093/mnras/stu2500 2015MNRAS.447.2123C Google Scholar
Cappelluti, N., et al. 2016, ApJ, 823, 95 10.3847/0004-637X/823/2/95 2016ApJ...823...95C Google Scholar
Carniani, S., et al. 2016, A&A, 591, A28 10.1051/0004-6361/201528037 2016A&A...591A..28C Google Scholar
Cen, R., & Riquelme, M. A. 2008, ApJ, 674, 644 10.1086/524724 2008ApJ...674..644C Google Scholar
Chen, K.-J., Whalen, D. J., Wollenberg, K. M. J., Glover, S. C. O., & Klessen, R. S. 2016, arXiv:1610.003892016arXiv161000389C Google Scholar
Chon, S., Hirano, S., Hosokawa, T., & Yoshida, N. 2016, ApJ, 832, 134 10.3847/0004-637X/832/2/134 2016ApJ...832..134C Google Scholar
Cicone, C., et al. 2015, A&A, 574, A14 10.1051/0004-6361/201424980 2015A&A...574A..14C Google Scholar
Ciotti, L., Ostriker, J. P., & Proga, D. 2009, ApJ, 699, 89 10.1088/0004-637X/699/1/89 2009ApJ...699...89C CrossRefGoogle Scholar
Ciotti, L., Ostriker, J. P., & Proga, D. 2010, ApJ, 717, 708 10.1088/0004-637X/717/2/708 2010ApJ...717..708C Google Scholar
Clark, P. C., Glover, S. C. O., & Klessen, R. S. 2008, ApJ, 672, 757 10.1086/524187 2008ApJ...672..757C Google Scholar
Clark, P. C., Glover, S. C. O., Smith, R. J., Greif, T. H., Klessen, R. S., & Bromm, V. 2011, Science, 331, 1040 10.1126/science.1198027 2011Sci...331.1040C Google Scholar
Costa, T., Sijacki, D., & Haehnelt, M. G. 2014, MNRAS, 444, 2355 10.1093/mnras/stu1632 2014MNRAS.444.2355C Google Scholar
Costa, T., Sijacki, D., & Haehnelt, M. G. 2015, MNRAS, 448, L30 10.1093/mnrasl/slu193 2015MNRAS.448L..30C Google Scholar
Cresci, G., et al. 2015, A&A, 582, A63 10.1051/0004-6361/201526581 2015A&A...582A..63C Google Scholar
D’Aloisio, A., Upton Sanderbeck, P. R., McQuinn, M., Trac, H., & Shapiro, P. R. 2016, MNRAS, 468, 4691 10.1093/mnras/stx711 2016arXiv160706467D Google Scholar
Davies, M. B., Miller, M. C., & Bellovary, J. M. 2011, ApJ, 740, L42 10.1088/2041-8205/740/2/L42 2011ApJ...740L..42D Google Scholar
de Bennassuti, M., Salvadori, S., Schneider, R., Valiante, R., & Omukai, K. 2017, MNRAS, 465, 926 10.1093/mnras/stw2687 2017MNRAS.465..926D Google Scholar
De Rosa, G., Decarli, R., Walter, F., Fan, X., Jiang, L., Kurk, J., Pasquali, A., & Rix, H. W. 2011, ApJ, 739, 56 10.1088/0004-637X/739/2/56 2011ApJ...739...56D Google Scholar
De Rosa, G., et al. 2014, ApJ, 790, 145 10.1088/0004-637X/790/2/145 2014ApJ...790..145D Google Scholar
Devecchi, B., & Volonteri, M. 2009, ApJ, 694, 302 10.1088/0004-637X/694/1/302 2009ApJ...694..302D Google Scholar
Devecchi, B., Volonteri, M., Colpi, M., & Haardt, F. 2010, MNRAS, 409, 1057 10.1111/j.1365-2966.2010.17363.x 2010MNRAS.409.1057D Google Scholar
Devecchi, B., Volonteri, M., Rossi, E. M., Colpi, M., & Portegies Zwart, S. 2012, MNRAS, 421, 1465 10.1111/j.1365-2966.2012.20406.x 2012MNRAS.421.1465D Google Scholar
Di Matteo, T., Croft, R. A. C., Feng, Y., Waters, D., & Wilkins, S. 2016, MNRAS, 467, 4243 CrossRefGoogle Scholar
Di Matteo, T., Khandai, N., DeGraf, C., Feng, Y., Croft, R. A. C., Lopez, J., & Springel, V. 2012, ApJ, 745, L29 10.1088/2041-8205/745/2/L29 2012ApJ...745L..29D CrossRefGoogle Scholar
Di Matteo, T., Springel, V., & Hernquist, L. 2005, Nature, 433, 604 10.1038/nature03335 2005Natur.433..604D Google Scholar
Dijkstra, M., Ferrara, A., & Mesinger, A. 2014, MNRAS, 442, 2036 10.1093/mnras/stu1007 2014MNRAS.442.2036D Google Scholar
Dijkstra, M., Gronke, M., & Sobral, D. 2016, ApJ, 823, 74 10.3847/0004-637X/823/2/74 2016ApJ...823...74D Google Scholar
Dijkstra, M., Haiman, Z., Mesinger, A., & Wyithe, J. S. B. 2008, MNRAS, 391, 1961 10.1111/j.1365-2966.2008.14031.x 2008MNRAS.391.1961D Google Scholar
Dolgov, A. D. 2016, arXiv:1605.067492016arXiv160506749D Google Scholar
Downes, D., & Solomon, P. M. 1998, ApJ, 507, 615 10.1086/306339 1998ApJ...507..615D Google Scholar
Dubois, Y., Volonteri, M., & Silk, J. 2014a, MNRAS, 440, 1590 10.1093/mnras/stu373 2014MNRAS.440.1590D Google Scholar
Dubois, Y., et al. 2014b, MNRAS, 444, 1453 10.1093/mnras/stu1227 2014MNRAS.444.1453D Google Scholar
Dwek, E., & Cherchneff, I. 2011, ApJ, 727, 63 10.1088/0004-637X/727/2/63 2011ApJ...727...63D Google Scholar
Dwek, E., Galliano, F., & Jones, A. P. 2007, ApJ, 662, 927 10.1086/518430 2007ApJ...662..927D Google Scholar
Eisenstein, D. J., & Loeb, A. 1995, ApJ, 443, 11 10.1086/175498 1995ApJ...443...11E Google Scholar
Fan, X., Strauss, M. A., Richards, G. T., Hennawi, J. F., Becker, R. H., White, R. L., & Diamond-Stanic, A. M. 2006, AJ, 131, 1203 10.1086/500296 2006AJ....131.1203F Google Scholar
Fan, X., et al. 2001, AJ, 122, 2833 10.1086/324111 2001AJ....122.2833F Google Scholar
Fan, X., et al. 2004, AJ, 128, 515 10.1086/422434 2004AJ....128..515F Google Scholar
Farrah, D., et al. 2012, ApJ, 745, 178 10.1088/0004-637X/745/2/178 2012ApJ...745..178F Google Scholar
Feng, Y., Di Matteo, T., Croft, R., & Khandai, N. 2014a, MNRAS, 440, 1865 10.1093/mnras/stu432 2014MNRAS.440.1865F CrossRefGoogle Scholar
Feng, Y., Di Matteo, T., Croft, R., & Khandai, N. 2014b, MNRAS, 440, 1865 10.1093/mnras/stu432 2014MNRAS.440.1865F Google Scholar
Feng, Y., Di Matteo, T., Croft, R., Tenneti, A., Bird, S., Battaglia, N., & Wilkins, S. 2015, ApJ, 808, L17 10.1088/2041-8205/808/1/L17 2015ApJ...808L..17F Google Scholar
Feng, Y., Di-Matteo, T., Croft, R. A., Bird, S., Battaglia, N., & Wilkins, S. 2016, MNRAS, 455, 2778 10.1093/mnras/stv2484 2016MNRAS.455.2778F Google Scholar
Ferrara, A., Salvadori, S., Yue, B., & Schleicher, D. 2014, MNRAS, 443, 2410 10.1093/mnras/stu1280 2014MNRAS.443.2410F Google Scholar
Ferrara, A., Viti, S., & Ceccarelli, C. 2016, MNRAS, 463, L112 10.1093/mnrasl/slw165 2016MNRAS.463L.112F Google Scholar
Feruglio, C., Maiolino, R., Piconcelli, E., Menci, N., Aussel, H., Lamastra, A., & Fiore, F. 2010, A&A, 518, L155 10.1051/0004-6361/201015164 2010A&A...518L.155F Google Scholar
Feruglio, C., et al. 2015, A&A, 583, A99 10.1051/0004-6361/201526020 2015A&A...583A..99F Google Scholar
Fumagalli, M., O’Meara, J. M., & Prochaska, J. X. 2011, Science, 334, 1245 10.1126/science.1213581 2011Sci...334.1245F Google Scholar
Gaibler, V., Khochfar, S., Krause, M., & Silk, J. 2012, MNRAS, 425, 438 10.1111/j.1365-2966.2012.21479.x 2012MNRAS.425..438G Google Scholar
Gall, C., Andersen, A. C., & Hjorth, J. 2011b, A&A, 528, A14 10.1051/0004-6361/201015605 2011A&A...528A..14G Google Scholar
Gall, C., Hjorth, J., & Andersen, A. C. 2011a, A&A Rev., 19, 43 10.1007/s00159-011-0043-7 2011A&ARv..19...43G Google Scholar
Gallerani, S., et al. 2010, A&A, 523, A85 10.1051/0004-6361/201014721 2010A&A...523A..85G Google Scholar
Gallerani, S., Fan, X., Maiolino, R., & Pacucci, F. 2017, PASA, 34, 22 10.1017/pasa.2017.14 Google Scholar
Giallongo, E., et al. 2015, A&A, 578, A83 10.1051/0004-6361/201425334 2015A&A...578A..83G Google Scholar
Glover, S. 2013, in Astrophysics and Space Science Library, Vol. 396, The First Galaxies, eds. Wiklind, T., Mobasher, B., & Bromm, V. (Berlin Heidelberg: Springer-Verlag), 103 10.1007/978-3-642-32362-1_3 Google Scholar
Glover, S. C. O. 2015a, MNRAS, 451, 2082 10.1093/mnras/stv1059 2015MNRAS.451.2082G Google Scholar
Glover, S. C. O. 2015b, MNRAS, 453, 2901 10.1093/mnras/stv1781 2015MNRAS.453.2901G Google Scholar
Granato, G. L., De Zotti, G., Silva, L., Bressan, A., & Danese, L. 2004, ApJ, 600, 580 10.1086/379875 2004ApJ...600..580G Google Scholar
Greene, J. E. 2012, NatCo, 3, 1304 10.1038/ncomms2314 2012NatCo...3E1304G Google Scholar
Greif, T. H., & Bromm, V. 2006, MNRAS, 373, 128 10.1111/j.1365-2966.2006.11017.x 2006MNRAS.373..128G Google Scholar
Greif, T. H., Bromm, V., Clark, P. C., Glover, S. C. O., Smith, R. J., Klessen, R. S., Yoshida, N., & Springel, V. 2012, MNRAS, 424, 399 10.1111/j.1365-2966.2012.21212.x 2012MNRAS.424..399G Google Scholar
Greif, T. H., Johnson, J. L., Bromm, V., & Klessen, R. S. 2007, ApJ, 670, 1 10.1086/522028 2007ApJ...670....1G Google Scholar
Greif, T. H., Springel, V., White, S. D. M., Glover, S. C. O., Clark, P. C., Smith, R. J., Klessen, R. S., & Bromm, V. 2011, ApJ, 737, 75 10.1088/0004-637X/737/2/75 2011ApJ...737...75G Google Scholar
Guo, Y., et al. 2013, ApJS, 207, 24 10.1088/0067-0049/207/2/24 2013ApJS..207...24G Google Scholar
Habouzit, M., Volonteri, M., & Dubois, Y. 2016a, MNRAS, 468, 3935 10.1093/mnras/stx666 2016arXiv160509394 Google Scholar
Habouzit, M., Volonteri, M., Latif, M., Dubois, Y., & Peirani, S. 2016c, MNRAS, 463, 529 10.1093/mnras/stw1924 2016MNRAS.463..529H Google Scholar
Habouzit, M., et al. 2016b, MNRAS, 456, 1901 10.1093/mnras/stv2740 2016MNRAS.456.1901H Google Scholar
Haemmerlé, L., Woods, T. E., Klessen, R. S., Heger, A., & Whalen, D. J. 2017, arXiv:1705.093012017arXiv170509301H Google Scholar
Haiman, Z. 2004, ApJ, 613, 36 10.1086/422910 2004ApJ...613...36H Google Scholar
Haiman, Z. 2012, arXiv: 1203.60752012arXiv1203.6075H Google Scholar
Haiman, Z. 2013, in Astrophysics and Space Science Library, Vol. 396, The First Galaxies, eds. Wiklind, T., Mobasher, B., & Bromm, V. (Berlin Heidelberg: Springer-Verlag), 293 10.1007/978-3-642-32362-1_6 Google Scholar
Hartwig, T., Glover, S. C. O., Klessen, R. S., Latif, M. A., & Volonteri, M. 2015, MNRAS, 452, 1233 10.1093/mnras/stv1368 2015MNRAS.452.1233H Google Scholar
Hartwig, T., et al. 2016, MNRAS, 462, 2184 10.1093/mnras/stw1775 2016MNRAS.462.2184H Google Scholar
Heger, A., Fryer, C. L., Woosley, S. E., Langer, N., & Hartmann, D. H. 2003, ApJ, 591, 288 10.1086/375341 2003ApJ...591..288H Google Scholar
Heger, A., & Woosley, S. E. 2010, ApJ, 724, 341 10.1088/0004-637X/724/1/341 2010ApJ...724..341H Google Scholar
Hirano, S., Hosokawa, T., Yoshida, N., Omukai, K., & Yorke, H. W. 2015, MNRAS, 448, 568 10.1093/mnras/stv044 2015MNRAS.448..568H Google Scholar
Hirano, S., Hosokawa, T., Yoshida, N., Umeda, H., Omukai, K., Chiaki, G., & Yorke, H. W. 2014, ApJ, 781, 60 10.1088/0004-637X/781/2/60 2014ApJ...781...60H Google Scholar
Hirashita, H., & Ferrara, A. 2002, MNRAS, 337, 921 10.1046/j.1365-8711.2002.05968.x 2002MNRAS.337..921H Google Scholar
Hopkins, A. M. 2004, ApJ, 615, 209 10.1086/424032 2004ApJ...615..209H Google Scholar
Hopkins, P. F., & Elvis, M. 2010, MNRAS, 401, 7 10.1111/j.1365-2966.2009.15643.x 2010MNRAS.401....7H Google Scholar
Hosokawa, T., Hirano, S., Kuiper, R., Yorke, H. W., Omukai, K., & Yoshida, N. 2016, ApJ, 824, 119 10.3847/0004-637X/824/2/119 2016ApJ...824..119H Google Scholar
Hosokawa, T., Omukai, K., Yoshida, N., & Yorke, H. W. 2011, Science, 334, 1250 10.1126/science.1207433 2011Sci...334.1250H Google Scholar
Hosokawa, T., Yorke, H. W., Inayoshi, K., Omukai, K., & Yoshida, N. 2013, ApJ, 778, 178 10.1088/0004-637X/778/2/178 2013Apj...778.178H Google Scholar
Husband, K., Bremer, M. N., Stanway, E. R., Davies, L. J. M., Lehnert, M. D., & Douglas, L. S. 2013, MNRAS, 432, 2869 10.1093/mnras/stt642 2013MNRAS.432.2869H CrossRefGoogle Scholar
Iliev, I. T., Mellema, G., Shapiro, P. R., & Pen, U.-L. 2007, MNRAS, 376, 534 10.1111/j.1365-2966.2007.11482.x 2007MNRAS.376..534I Google Scholar
Inayoshi, K., & Omukai, K. 2011, MNRAS, 416, 2748 Google Scholar
Inayoshi, K., & Omukai, K. 2012, MNRAS, 422, 2539 10.1111/j.1365-2966.2012.20812.x 2012MNRAS.422.2539I Google Scholar
Inayoshi, K., Omukai, K., & Tasker, E. 2014, MNRAS, 445, L109 10.1093/mnrasl/slu151 2014MNRAS.445L.109I Google Scholar
Inayoshi, K., & Tanaka, T. L. 2015, MNRAS, 450, 4350 10.1093/mnras/stv871 2015MNRAS.450.4350I Google Scholar
Inayoshi, K., Visbal, E., & Kashiyama, K. 2015, MNRAS, 453, 1692 10.1093/mnras/stv1654 2015MNRAS.453.1692I Google Scholar
Jiang, L., Fan, X., Vestergaard, M., Kurk, J. D., Walter, F., Kelly, B. C., & Strauss, M. A. 2007, AJ, 134, 1150 10.1086/520811 2007AJ....134.1150J Google Scholar
Jiang, L., McGreer, I. D., Fan, X., Bian, F., Cai, Z., Clément, B., Wang, R., & Fan, Z. 2015, AJ, 149, 188 10.1088/0004-6256/149/6/188 2015AJ....149..188J Google Scholar
Jiang, L., et al. 2009, AJ, 138, 305 10.1088/0004-6256/138/1/305 2009AJ....138..305J Google Scholar
Jiang, L., et al. 2016, ApJ, 833, 222 10.3847/1538-4357/833/2/222 2016ApJ...833..222J Google Scholar
Jiang, Y.-F., Stone, J. M., & Davis, S. W. 2014, ApJ, 796, 106 10.1088/0004-637X/796/2/106 2014ApJ...796..106J Google Scholar
Joggerst, C. C., Almgren, A., Bell, J., Heger, A., Whalen, D., & Woosley, S. E. 2010, ApJ, 709, 11 10.1088/0004-637X/709/1/11 2010ApJ...709...11J Google Scholar
Johnson, J. L., & Haardt, F. 2016, PASA, 33, 7 10.1017/pasa.2016.4 2016PASA...33....7J Google Scholar
Johnson, J. L., Whalen, D. J., Agarwal, B., Paardekooper, J.-P., & Khochfar, S. 2014, MNRAS, 445, 686 10.1093/mnras/stu1676 2014MNRAS.445..686J Google Scholar
Johnson, J. L., Whalen, D. J., Fryer, C. L., & Li, H. 2012, ApJ, 750, 66 10.1088/0004-637X/750/1/66 2012ApJ...750...66J Google Scholar
Johnson, J. L., Whalen, D. J., Li, H., & Holz, D. E. 2013, ApJ, 771, 116 10.1088/0004-637X/771/2/116 2013ApJ...771..116J Google Scholar
Juarez, Y., Maiolino, R., Mujica, R., Pedani, M., Marinoni, S., Nagao, T., Marconi, A., & Oliva, E. 2009, A&A, 494, L25 10.1051/0004-6361:200811415 2009A&A...494L..25J Google Scholar
Katz, H., Sijacki, D., & Haehnelt, M. G. 2015, MNRAS, 451, 2352 10.1093/mnras/stv1048 2015MNRAS.451.2352K Google Scholar
Kennicutt, R. C. Jr 1998, ApJ, 498, 541 10.1086/305588 1998ApJ...498..541K Google Scholar
Khandai, N., Feng, Y., DeGraf, C., Di Matteo, T., & Croft, R. A. C. 2012, MNRAS, 423, 2397 10.1111/j.1365-2966.2012.21047.x 2012MNRAS.423.2397K Google Scholar
Kim, S., et al. 2009a, ApJ, 695, 809 10.1088/0004-637X/695/2/809 2009ApJ...695..809K Google Scholar
Kim, S., et al. 2009b, ApJ, 695, 809 Google Scholar
Kitayama, T., Yoshida, N., Susa, H., & Umemura, M. 2004, ApJ, 613, 631 10.1086/423313 2004ApJ...613..631K Google Scholar
Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511 10.1146/annurev-astro-082708-101811 2013ARA&A..51..511K Google Scholar
Lacey, C., & Cole, S. 1993, MNRAS, 262, 627 10.1093/mnras/262.3.627 1993MNRAS.262..627L Google Scholar
Lamastra, A., Menci, N., Maiolino, R., Fiore, F., & Merloni, A. 2010, MNRAS, 405, 29 10.1111/j.1365-2966.2010.16439.x 2010MNRAS.405...29L Google Scholar
Laporte, N., et al. 2012, A&A, 542, L31 10.1051/0004-6361/201219486 2012A&A...542L..31L Google Scholar
Latif, M. A., & Ferrara, A. 2016, PASA, 33, 51 10.1017/pasa.2016.41 2016PASA...33...51L Google Scholar
Latif, M. A., & Volonteri, M. 2015, MNRAS, 452, 1026 10.1093/mnras/stv1337 2015MNRAS.452.1026L Google Scholar
Latif, M. A., Bovino, S., Van Borm, C., Grassi, T., Schleicher, D. R. G., & Spaans, M. 2014, MNRAS, 443, 1979 10.1093/mnras/stu1230 2014MNRAS.443.1979L Google Scholar
Latif, M. A., Omukai, K., Habouzit, M., Schleicher, D. R. G., & Volonteri, M. 2016, ApJ, 823, 40 10.3847/0004-637X/823/1/40 2016ApJ...823...40L Google Scholar
Latif, M. A., Schleicher, D. R. G., Schmidt, W., & Niemeyer, J. 2013a, MNRAS, 433, 1607 10.1093/mnras/stt834 2013MNRAS.433.1607L Google Scholar
Latif, M. A., Schleicher, D. R. G., Schmidt, W., & Niemeyer, J. C. 2013b, MNRAS, 436, 2989 10.1093/mnras/stt1786 2013MNRAS.436.2989L Google Scholar
Lauer, T. R., Tremaine, S., Richstone, D., & Faber, S. M. 2007, ApJ, 670, 249 10.1086/522083 2007ApJ...670..249L Google Scholar
Li, Y., et al. 2007, ApJ, 665, 187 10.1086/519297 2007ApJ...665..187L Google Scholar
Li, Y., et al. 2008, ApJ, 678, 41 10.1086/529364 2008ApJ...678...41L Google Scholar
Lodato, G., & Natarajan, P. 2006, MNRAS, 371, 1813 10.1111/j.1365-2966.2006.10801.x 2006MNRAS.371.1813L Google Scholar
Lodato, G., & Natarajan, P. 2007, MNRAS, 377, L64 10.1111/j.1745-3933.2007.00304.x 2007MNRAS.377L..64L Google Scholar
Loeb, A., & Rasio, F. A. 1994, ApJ, 432, 52 10.1086/174548 1994ApJ...432...52L Google Scholar
Lupi, A., Colpi, M., Devecchi, B., Galanti, G., & Volonteri, M. 2014, MNRAS, 442, 3616 10.1093/mnras/stu1120 2014MNRAS.442.3616L Google Scholar
Lupi, A., Haardt, F., Dotti, M., Fiacconi, D., Mayer, L., & Madau, P. 2016, MNRAS, 456, 2993 10.1093/mnras/stv2877 2016MNRAS.456.2993L Google Scholar
Lyu, J., Rieke, G. H., & Alberts, S. 2016, ApJ, 816, 85 10.3847/0004-637X/816/2/85 2016ApJ...816...85L Google Scholar
Machacek, M. E., Bryan, G. L., & Abel, T. 2001, ApJ, 548, 509 10.1086/319014 2001ApJ...548..509M Google Scholar
Mackey, J., Bromm, V., & Hernquist, L. 2003, ApJ, 586, 1 10.1086/367613 2003ApJ...586....1M Google Scholar
Madau, P., & Haardt, F. 2015, ApJ, 813, L8 10.1088/2041-8205/813/1/L8 2015ApJ...813L...8M Google Scholar
Madau, P., Haardt, F., & Dotti, M. 2014, ApJ, 784, L38 10.1088/2041-8205/784/2/L38 2014ApJ...784L..38M Google Scholar
Madau, P., & Rees, M. J. 2001, ApJ, 551, L27 10.1086/319848 2001ApJ...551L..27M Google Scholar
Madau, P., Rees, M. J., Volonteri, M., Haardt, F., & Oh, S. P. 2004, ApJ, 604, 484 10.1086/381935 2004ApJ...604..484M Google Scholar
Magnelli, B., et al. 2012, A&A, 539, A155 10.1051/0004-6361/201118312 2012A&A...539A.155M Google Scholar
Maiolino, R., Schneider, R., Oliva, E., Bianchi, S., Ferrara, A., Mannucci, F., Pedani, M., & Roca Sogorb, M. 2004, Nature, 431, 533 10.1038/nature02930 2004Natur.431..533M Google Scholar
Maiolino, R., et al. 2005, A&A, 440, L51 10.1051/0004-6361:200500165 2005A&A...440L..51M Google Scholar
Maiolino, R., et al. 2007, A&A, 472, L33 10.1051/0004-6361:20078136 2007A&A...472L..33M Google Scholar
Maiolino, R., et al. 2012, MNRAS, 425, L66 10.1111/j.1745-3933.2012.01303.x 2012MNRAS.425L..66M Google Scholar
Makiya, R., et al. 2016, PASJ, 68, 25 10.1093/pasj/psw005 2016PASJ...68...25M Google Scholar
Mannucci, F., Buttery, H., Maiolino, R., Marconi, A., & Pozzetti, L. 2007, A&A, 461, 423 10.1051/0004-6361:20065993 2007A&A...461..423M Google Scholar
Marassi, S., Schneider, R., Limongi, M., Chieffi, A., Bocchio, M., & Bianchi, S. 2015, MNRAS, 454, 4250 10.1093/mnras/stv226 2015MNRAS.454.4250M Google Scholar
Matsuoka, K., Nagao, T., Maiolino, R., Marconi, A., & Taniguchi, Y. 2009, A&A, 503, 721 10.1051/0004-6361/200811478 2009A&A...503..721M Google Scholar
Matthee, J., Sobral, D., Santos, S., Röttgering, H., Darvish, B., & Mobasher, B. 2015, MNRAS, 451, 400 10.1093/mnras/stv947 2015MNRAS.451..400M Google Scholar
Mattsson, L. 2011, MNRAS, 414, 781 10.1111/j.1365-2966.2011.18447.x 2011MNRAS.414..781M Google Scholar
Mayer, L., Kazantzidis, S., Escala, A., & Callegari, S. 2010, Nature, 466, 1082 10.1038/nature09294 2010Natur.466.1082M Google Scholar
Mayer, L., Kazantzidis, S., Madau, P., Colpi, M., Quinn, T., & Wadsley, J. 2007, Science, 316, 1874 10.1126/science.1141858 2007Sci...316.1874M Google Scholar
Mazzucchelli, C., Bañados, E., Decarli, R., Farina, E. P., Venemans, B. P., Walter, F., & Overzier, R. 2017, ApJ, 834, 83 10.3847/1538-4357/834/1/83 2017ApJ...834...83M Google Scholar
McGreer, I. D., Eftekharzadeh, S., Myers, A. D., & Fan, X. 2016, AJ, 151, 61 10.3847/0004-6256/151/3/61 2016AJ....151...61M Google Scholar
Michałowski, M. J., Murphy, E. J., Hjorth, J., Watson, D., Gall, C., & Dunlop, J. S. 2010, A&A, 522, A15 10.1051/0004-6361/201014902 2010A&A...522A..15M Google Scholar
Milosavljević, M., Bromm, V., Couch, S. M., & Oh, S. P. 2009b, ApJ, 698, 766 10.1088/0004-637X/698/1/766 2009ApJ...698..766M Google Scholar
Milosavljević, M., Couch, S. M., & Bromm, V. 2009a, ApJ, 696, L146 10.1088/0004-637X/696/2/L146 2009ApJ...696L.146M Google Scholar
Mineshige, S., Kawaguchi, T., Takeuchi, M., & Hayashida, K. 2000, PASJ, 52, 499 10.1093/pasj/52.3.499 2000PASJ...52..499M Google Scholar
Morgan, H. L., & Edmunds, M. G. 2003, MNRAS, 343, 427 10.1046/j.1365-8711.2003.06681.x 2003MNRAS.343..427M Google Scholar
Morselli, L., et al. 2014, A&A, 568, A1 10.1051/0004-6361/201423853 2014A&A...568A...1M Google Scholar
Mortlock, D. J., et al. 2011, Nature, 474, 616 10.1038/nature10159 2011Natur.474..616M Google Scholar
Muratov, A. L., Keres, D., Faucher-Giguere, C.-A., Hopkins, P. F., Quataert, E., & Murray, N. 2015, MNRAS, 454, 2691 Google Scholar
Nagao, T., Marconi, A., & Maiolino, R. 2006, A&A, 447, 157 10.1051/0004-6361:20054024 2006A&A...447..157N Google Scholar
Natarajan, P. 2011, arXiv:1105.49022011arXiv1105.4902N Google Scholar
Nesvadba, N. P. H., Polletta, M., Lehnert, M. D., Bergeron, J., De Breuck, C., Lagache, G., & Omont, A. 2011, MNRAS, 415, 2359 10.1111/j.1365-2966.2011.18862.x 2011MNRAS.415.2359N Google Scholar
Nesvadba, N. P. H., et al. 2010, A&A, 521, A65 10.1051/0004-6361/200913333 2010A&A...521A..65N Google Scholar
Netzer, H., Mor, R., Trakhtenbrot, B., Shemmer, O., & Lira, P. 2014, ApJ, 791, 34 10.1088/0004-637X/791/1/34 2014ApJ...791...34N Google Scholar
Nozawa, T., Kozasa, T., Habe, A., Dwek, E., Umeda, H., Tominaga, N., Maeda, K., & Nomoto, K. 2007, ApJ, 666, 955 10.1086/520621 2007ApJ...666..955N Google Scholar
O’Shea, B. W., & Norman, M. L. 2007, ApJ, 654, 66 10.1086/509250 2007ApJ...654...66O Google Scholar
O’Shea, B. W., & Norman, M. L. 2008, ApJ, 673, 14 10.1086/524006 2008ApJ...673...14O Google Scholar
Omukai, K. 2000, ApJ, 534, 809 10.1086/308776 2000ApJ...534..809O Google Scholar
Omukai, K. 2001, ApJ, 546, 635 10.1086/318296 2001ApJ...546..635O Google Scholar
Omukai, K., Schneider, R., & Haiman, Z. 2008, ApJ, 686, 801 10.1086/591636 2008ApJ...686..801O Google Scholar
Omukai, K., Tsuribe, T., Schneider, R., & Ferrara, A. 2005, ApJ, 626, 627 10.1086/429955 2005ApJ...626..627O Google Scholar
Overzier, R. A., Guo, Q., Kauffmann, G., De Lucia, G., Bouwens, R., & Lemson, G. 2009, MNRAS, 394, 577 10.1111/j.1365-2966.2008.14264.x 2009MNRAS.394..577O Google Scholar
Pacucci, F., Ferrara, A., Grazian, A., Fiore, F., Giallongo, E., & Puccetti, S. 2016, MNRAS, 459, 1432 10.1093/mnras/stw725 2016MNRAS.459.1432P Google Scholar
Pacucci, F., Pallottini, A., Ferrara, A., & Gallerani, S. 2017, MNRAS, 468, L77 10.1093/mnrasl/slx029 2017MNRAS.468L..77P Google Scholar
Pallottini, A., Ferrara, A., Gallerani, S., Salvadori, S., & D’Odorico, V. 2014, MNRAS, 440, 2498 10.1093/mnras/stu451 2014MNRAS.440.2498P Google Scholar
Pallottini, A., et al. 2015, MNRAS, 453, 2465 10.1093/mnras/stv1795 2015MNRAS.453.2465P Google Scholar
Park, K., & Ricotti, M. 2011, ApJ, 739, 2 10.1088/0004-637X/739/1/2 2011ApJ...739....2P Google Scholar
Park, K., & Ricotti, M. 2012, ApJ, 747, 9 10.1088/0004-637X/747/1/9 2012ApJ...747....9P Google Scholar
Park, K., & Ricotti, M. 2013, ApJ, 767, 163 10.1088/0004-637X/767/2/163 2013ApJ...767..163P Google Scholar
Parkinson, H., Cole, S., & Helly, J. 2008, MNRAS, 383, 557 10.1111/j.1365-2966.2007.12517.x 2008MNRAS.383..557P Google Scholar
Pawlik, A. H., Bromm, V., & Milosavljević, M. 2014, Mem. Soc. Astron. Italiana, 85, 565 2014MmSAI..85..565P Google Scholar
Peebles, P. J. E. 1969, ApJ, 155, 393 10.1086/149876 1969ApJ...155..393P Google Scholar
Peirani, S., et al. 2016, arXiv:1611.099222016arXiv161109922P Google Scholar
Pelupessy, F. I., Di Matteo, T., & Ciardi, B. 2007, ApJ, 665, 107 10.1086/519235 2007ApJ...665..107P Google Scholar
Peng, Y., Maiolino, R., & Cochrane, R. 2015, Nature, 521, 192 10.1038/nature14439 2015Natur.521..192P Google Scholar
Pentericci, L., et al. 2002, AJ, 123, 2151 10.1086/340077 2002AJ....123.2151P Google Scholar
Perley, D. A., et al. 2010, MNRAS, 406, 2473 10.1111/j.1365-2966.2010.16772.x 2010MNRAS.406.2473P Google Scholar
Petri, A., Ferrara, A., & Salvaterra, R. 2012, MNRAS, 422, 1690 10.1111/j.1365-2966.2012.20743.x 2012MNRAS.422.1690P Google Scholar
Pezzulli, E., Valiante, R., & Schneider, R. 2016, MNRAS, 458, 3047 10.1093/mnras/stw505 2016MNRAS.458.3047P Google Scholar
Pipino, A., Fan, X. L., Matteucci, F., Calura, F., Silva, L., Granato, G., & Maiolino, R. 2011, A&A, 525, A61 10.1051/0004-6361/201014843 2011A&A...525A..61P Google Scholar
Planck Collaboration et al. 2016, A&A, 594, A13 10.1051/0004-6361/201525830 2016A&A...594A..13P Google Scholar
Press, W. H., & Schechter, P. 1974, ApJ, 187, 425 10.1086/152650 1974ApJ...187..425P Google Scholar
Priddey, R. S., Isaak, K. G., McMahon, R. G., Robson, E. I., & Pearson, C. P. 2003, MNRAS, 344, L74 10.1046/j.1365-8711.2003.07076.x 2003MNRAS.344L..74P Google Scholar
Regan, J. A., & Haehnelt, M. G. 2009a, MNRAS, 393, 858 10.1111/j.1365-2966.2008.14088.x 2009MNRAS.393..858R Google Scholar
Regan, J. A., & Haehnelt, M. G. 2009b, MNRAS, 396, 343 10.1111/j.1365-2966.2009.14579.x 2009MNRAS.396..343R Google Scholar
Regan, J. A., Johansson, P. H., & Wise, J. H. 2014, ApJ, 795, 137 10.1088/0004-637X/795/2/137 2014ApJ...795..137R Google Scholar
Regan, J. A., Johansson, P. H., & Wise, J. H. 2016, MNRAS, 459, 3377 10.1093/mnras/stw899 2016MNRAS.459.3377R Google Scholar
Regan, J. A., Visbal, E., Wise, J. H., Haiman, Z., Johansson, P. H., & Bryan, G. L. 2017, NatAs, 1, 0075 10.1038/s41550-017-0075 2017NatAs...1E..75R Google Scholar
Reines, A. E., Greene, J. E., & Geha, M. 2013, ApJ, 775, 116 10.1088/0004-637X/775/2/116 2013ApJ...775..116R Google Scholar
Ritter, J. S., Safranek-Shrader, C., Gnat, O., Milosavljević, M., & Bromm, V. 2012, ApJ, 761, 56 10.1088/0004-637X/761/1/56 2012ApJ...761...56R Google Scholar
Robson, I., Priddey, R. S., Isaak, K. G., & McMahon, R. G. 2004, MNRAS, 351, L29 10.1111/j.1365-2966.2004.07923.x 2004MNRAS.351L..29R Google Scholar
Rowlands, K., Gomez, H. L., Dunne, L., Aragón-Salamanca, A., Dye, S., Maddox, S., da Cunha, E., & van der Werf, P. 2014, MNRAS, 441, 1040 10.1093/mnras/stu605 2014MNRAS.441.1040R Google Scholar
Sakurai, Y., Hosokawa, T., Yoshida, N., & Yorke, H. W. 2015, MNRAS, 452, 755 Google Scholar
Sani, E., Marconi, A., Hunt, L. K., & Risaliti, G. 2011, MNRAS, 413, 1479 10.1111/j.1365-2966.2011.18229.x 2011MNRAS.413.1479S Google Scholar
Santini, P., et al. 2010, A&A, 518, L154 10.1051/0004-6361/201014748 2010A&A...518L.154S Google Scholar
Schaerer, D. 2002, A&A, 382, 28 10.1051/0004-6361:20011619 2002A&A...382...28S Google Scholar
Schauer, A. T. P., Regan, J., Glover, S. C. O., & Klessen, R. S. 2017a, arXiv:1705.023472017arXiv170502347S Google Scholar
Schauer, A. T. P., Whalen, D. J., Glover, S. C. O., & Klessen, R. S. 2015, MNRAS, 454, 2441 10.1093/mnras/stv2117 2015MNRAS.454.2441S Google Scholar
Schauer, A. T. P., et al. 2017b, MNRAS, 467, 2288 10.1093/mnras/stx264 2017MNRAS.467.2288S Google Scholar
Schneider, R., Bianchi, S., Valiante, R., Risaliti, G., & Salvadori, S. 2015, A&A, 579, A60 10.1051/0004-6361/201526105 2015A&A...579A..60S Google Scholar
Schneider, R., Ferrara, A., Natarajan, P., & Omukai, K. 2002, ApJ, 571, 30 10.1086/339917 2002ApJ...571...30S Google Scholar
Schneider, R., Ferrara, A., Salvaterra, R., Omukai, K., & Bromm, V. 2003, Nature, 422, 869 2003Natur.422..869S Google Scholar
Schneider, R., Omukai, K., Inoue, A. K., & Ferrara, A. 2006b, MNRAS, 369, 1437 10.1111/j.1365-2966.2006.10391.x 2006MNRAS.369.1437S Google Scholar
Schneider, R., Omukai, K., Bianchi, S., & Valiante, R. 2012, MNRAS, 419, 1566 10.1111/j.1365-2966.2011.19818.x 2012MNRAS.419.1566S Google Scholar
Schneider, R., Salvaterra, R., Ferrara, A., & Ciardi, B. 2006a, MNRAS, 369, 825 10.1111/j.1365-2966.2006.10331.x 2006MNRAS.369..825S Google Scholar
Schnittman, J. D., Buonanno, A., van Meter, J. R., Baker, J. G., Boggs, W. D., Centrella, J., Kelly, B. J., & McWilliams, S. T. 2008, Phys. Rev. D, 77, 044031 10.1103/PhysRevD.77.044031 2008PhRvD..77d4031S Google Scholar
Shang, C., Bryan, G. L., & Haiman, Z. 2010, MNRAS, 402, 1249 10.1111/j.1365-2966.2009.15960.x 2010MNRAS.402.1249S Google Scholar
Shapiro, S. L. 2005, ApJ, 620, 59 10.1086/427065 2005ApJ...620...59S Google Scholar
Shirakata, H., et al. 2016, MNRAS, 461, 4389 10.1093/mnras/stw1798 2016MNRAS.461.4389S Google Scholar
Sijacki, D., Springel, V., & Haehnelt, M. G. 2009, MNRAS, 400, 100 10.1111/j.1365-2966.2009.15452.x 2009MNRAS.400..100S Google Scholar
Silk, J. 2013, ApJ, 772, 112 10.1088/0004-637X/772/2/112 2013ApJ...772..112S Google Scholar
Silk, J., & Rees, M. J. 1998, A&A, 331, L1 1998A&A...331L...1S Google Scholar
Simpson, C., Mortlock, D., Warren, S., Cantalupo, S., Hewett, P., McLure, R., McMahon, R., & Venemans, B. 2014, MNRAS, 442, 3454 10.1093/mnras/stu1116 2014MNRAS.442.3454S Google Scholar
Smidt, J., Wiggins, B. K., & Johnson, J. L. 2016, ApJ, 829, L6 10.3847/2041-8205/829/1/L6 2016ApJ...829L...6S Google Scholar
Smidt, J., Whalen, D. J., Johnson, J. L., & Li, H. 2017, arXiv:1703.004492017arXiv170300449S Google Scholar
Smith, A., Bromm, V., & Loeb, A. 2016, MNRAS, 460, 3143 10.1093/mnras/stw1129 2016MNRAS.460.3143S Google Scholar
Smith, B. D., Wise, J. H., O’Shea, B. W., Norman, M. L., & Khochfar, S. 2015, MNRAS, 452, 2822 10.1093/mnras/stv1509 2015MNRAS.452.2822S Google Scholar
Sobral, D., Matthee, J., Darvish, B., Schaerer, D., Mobasher, B., Röttgering, H. J. A., Santos, S., & Hemmati, S. 2015, ApJ, 808, 139 10.1088/0004-637X/808/2/139 2015ApJ...808..139S Google Scholar
Solomon, P. M., Downes, D., Radford, S. J. E., & Barrett, J. W. 1997, ApJ, 478, 144 1997ApJ...478..144S Google Scholar
Somerville, R. S., & Kolatt, T. S. 1999, MNRAS, 305, 1 10.1046/j.1365-8711.1999.02154.x 1999MNRAS.305....1S Google Scholar
Spaans, M., & Silk, J. 2006, ApJ, 652, 902 10.1086/508444 2006ApJ...652..902S Google Scholar
Springel, V., Di Matteo, T., & Hernquist, L. 2005a, MNRAS, 361, 776 10.1111/j.1365-2966.2005.09238.x 2005MNRAS.361..776S Google Scholar
Springel, V., et al. 2005b, Nature, 435, 629 10.1038/nature03597 2005Natur.435..629S Google Scholar
Stacy, A., Bromm, V., & Lee, A. T. 2016, MNRAS, 462, 1307 10.1093/mnras/stw1728 2016MNRAS.462.1307S Google Scholar
Stacy, A., Greif, T. H., & Bromm, V. 2010, MNRAS, 403, 45 10.1111/j.1365-2966.2009.16113.x 2010MNRAS.403...45S Google Scholar
Stacy, A., Greif, T. H., & Bromm, V. 2012, MNRAS, 422, 290 10.1111/j.1365-2966.2012.20605.x 2012MNRAS.422..290S Google Scholar
Stiavelli, M., et al. 2005a, ApJ, 622, L1 10.1086/429406 2005ApJ...622L...1S Google Scholar
Stiavelli, M., et al. 2005b, ApJ, 622, L1 Google Scholar
Stratta, G., Maiolino, R., Fiore, F., & D’Elia, V. 2007, ApJ, 661, L9 10.1086/518502 2007ApJ...661L...9S Google Scholar
Sugimura, K., Omukai, K., & Inoue, A. K. 2014, MNRAS, 445, 544 10.1093/mnras/stu1778 2014MNRAS.445..544S Google Scholar
Susa, H., Hasegawa, K., & Tominaga, N. 2014, ApJ, 792, 32 10.1088/0004-637X/792/1/32 2014ApJ...792...32S Google Scholar
Tanaka, T., & Haiman, Z. 2009, ApJ, 696, 1798 10.1088/0004-637X/696/2/1798 2009ApJ...696.1798T Google Scholar
Tornatore, L., Borgani, S., Dolag, K., & Matteucci, F. 2007, MNRAS, 382, 1050 10.1111/j.1365-2966.2007.12070.x 2007MNRAS.382.1050T Google Scholar
Trakhtenbrot, B., Lira, P., Netzer, H., Cicone, C., Maiolino, R., & Shemmer, O. 2017a, ApJ, 836, 8 10.3847/1538-4357/836/1/8 2017ApJ...836....8T Google Scholar
Trakhtenbrot, B., Volonteri, M., & Natarajan, P. 2017b, ApJ, 836, L1 10.3847/2041-8213/836/1/L1 2017ApJ...836L...1T Google Scholar
Trichas, M., et al. 2012, ApJS, 200, 17 10.1088/0067-0049/200/2/17 2012ApJS..200...17T Google Scholar
Turk, M. J., Abel, T., & O’Shea, B. 2009, Science, 325, 601 10.1126/science.1173540 2009Sci...325..601T Google Scholar
Umeda, H., Hosokawa, T., Omukai, K., & Yoshida, N. 2016, ApJL, 830, L34 10.3847/2041-8205/830/2/L34 2016ApJ...830...L34 Google Scholar
Utsumi, Y., Goto, T., Kashikawa, N., Miyazaki, S., Komiyama, Y., Furusawa, H., & Overzier, R. 2010, ApJ, 721, 1680 10.1088/0004-637X/721/2/1680 2010ApJ...721.1680U Google Scholar
Valiante, R., Schneider, R., Bianchi, S., & Andersen, A. C. 2009, MNRAS, 397, 1661 10.1111/j.1365-2966.2009.15076.x 2009MNRAS.397.1661V Google Scholar
Valiante, R., Schneider, R., Maiolino, R., Salvadori, S., & Bianchi, S. 2012, MNRAS, 427, L60 10.1111/j.1745-3933.2012.01345.x 2012MNRAS.427L..60V Google Scholar
Valiante, R., Schneider, R., Salvadori, S., & Bianchi, S. 2011, MNRAS, 416, 1916 10.1111/j.1365-2966.2011.19168.x 2011MNRAS.416.1916V Google Scholar
Valiante, R., Schneider, R., Salvadori, S., & Gallerani, S. 2014, MNRAS, 444, 2442 10.1093/mnras/stu1613 2014MNRAS.444.2442V Google Scholar
Valiante, R., Schneider, R., Volonteri, M., & Omukai, K. 2016, MNRAS, 457, 3356 10.1093/mnras/stw225 2016MNRAS.457.3356V Google Scholar
Venemans, B. P., Walter, F., Zschaechner, L., Decarli, R., De Rosa, G., Findlay, J. R., McMahon, R. G., & Sutherland, W. J. 2016, ApJ, 816, 37 10.3847/0004-637X/816/1/37 2016ApJ...816...37V Google Scholar
Venemans, B. P., et al. 2013, ApJ, 779, 24 10.1088/0004-637X/779/1/24 2013ApJ...779...24V Google Scholar
Venemans, B. P., et al. 2015, MNRAS, 453, 2259 10.1093/mnras/stv1774 2015MNRAS.453.2259V Google Scholar
Venemans, B. P., et al. 2017, ApJ, 837, 146 10.3847/1538-4357/aa62ac 2017ApJ...837..146V Google Scholar
Visbal, E., Haiman, Z., & Bryan, G. L. 2014, MNRAS, 445, 1056 10.1093/mnras/stu1794 2014MNRAS.445.1056V Google Scholar
Visbal, E., Haiman, Z., & Bryan, G. L. 2016, MNRAS, 460, L59 10.1093/mnrasl/slw071 2016MNRAS.460L..59V Google Scholar
Vito, F., et al. 2016, MNRAS, 463, 348 10.1093/mnras/stw1998 2016MNRAS.463..348V Google Scholar
Volonteri, M. 2010, ARA&A, 18, 279 10.1007/s00159-010-0029-x 2010A&ARv..18..279V Google Scholar
Volonteri, M., & Begelman, M. C. 2010, MNRAS, 409, 1022 Google Scholar
Volonteri, M., & Bellovary, J. 2012, RPPh, 75, 124901 10.1088/0034-4885/75/12/124901 2012RPPh...75l4901V Google Scholar
Volonteri, M., Bogdanović, T., Dotti, M., & Colpi, M. 2016a, IAUFM, 29, 285 10.1017/S1743921316005366 2016IAUFM..29B.285V Google Scholar
Volonteri, M., Dubois, Y., Pichon, C., & Devriendt, J. 2016b, MNRAS, 460, 2979 10.1093/mnras/stw1123 2016MNRAS.460.2979V Google Scholar
Volonteri, M., Haardt, F., & Madau, P. 2003, ApJ, 582, 559 10.1086/344675 2003ApJ...582..559V Google Scholar
Volonteri, M., & Rees, M. J. 2005a, ApJ, 633, 624 10.1086/466521 2005ApJ...633..624V Google Scholar
Volonteri, M., & Rees, M. J. 2005b, ApJ, 633, 624 10.1086/466521 2005ApJ...633..624V Google Scholar
Volonteri, M., & Rees, M. J. 2006a, ApJ, 650, 669 10.1086/507444 2006ApJ...650..669V Google Scholar
Volonteri, M., & Rees, M. J. 2006b, ApJ, 650, 669 10.1086/507444 2006ApJ...650..669V Google Scholar
Volonteri, M., Silk, J., & Dubus, G. 2015, ApJ, 804, 148 10.1088/0004-637X/804/2/148 2015ApJ...804..148V Google Scholar
Volonteri, M., & Stark, D. P. 2011, MNRAS, 417, 2085 10.1111/j.1365-2966.2011.19391.x 2011MNRAS.417.2085V Google Scholar
Wagner, A. Y., Umemura, M., & Bicknell, G. V. 2013, ApJ, 763, L18 10.1088/2041-8205/763/1/L18 2013ApJ...763L..18W Google Scholar
Walter, F., Carilli, C., Bertoldi, F., Menten, K., Cox, P., Lo, K. Y., Fan, X., & Strauss, M. A. 2004, ApJ, 615, L17 10.1086/426017 2004ApJ...615L..17W Google Scholar
Wang, J. X., Malhotra, S., & Rhoads, J. E. 2005, ApJ, 622, L77 10.1086/429617 2005ApJ...622L..77W Google Scholar
Wang, R., et al. 2008, ApJ, 687, 848 10.1086/591076 2008ApJ...687..848W Google Scholar
Wang, R., et al. 2010, ApJ, 714, 699 10.1088/0004-637X/714/1/699 2010ApJ...714..699W Google Scholar
Wang, R., et al. 2013, ApJ, 773, 44 10.1088/0004-637X/773/1/44 2013ApJ...773...44W Google Scholar
Weigel, A. K., Schawinski, K., Treister, E., Urry, C. M., Koss, M., & Trakhtenbrot, B. 2015, MNRAS, 448, 3167 10.1093/mnras/stv184 2015MNRAS.448.3167W Google Scholar
Whalen, D. J., & Fryer, C. L. 2012, ApJ, 756, L19 10.1088/2041-8205/756/1/L19 2012ApJ...756L..19W Google Scholar
Whalen, D., van Veelen, B., O’Shea, B. W., & Norman, M. L. 2008, ApJ, 682, 49 10.1086/589643 2008ApJ...682...49W Google Scholar
Willott, C. J., Crampton, D., Hutchings, J. B., Sawicki, M., Simard, L., Jarvis, M. J., McLure, R. J., & Percival, W. J. 2005, in Proc. of the MPA/ESO/MPE/USM Joint Astronomy Conference, Growing Black Holes: Accretion in a Cosmological Context, eds. Merloni, A., Nayakshin, S., & Sunyaev, R. V. (Berlin Heidelberg: Springer-Verlag), 102 10.1007/11403913_12 astro-ph/0410306 Google Scholar
Willott, C. J., McLure, R. J., & Jarvis, M. J. 2003, ApJ, 587, L15 10.1086/375126 2003ApJ...587L..15W Google Scholar
Willott, C. J., et al. 2010, AJ, 140, 546 10.1088/0004-6256/140/2/546 2010AJ....140..546W Google Scholar
Wise, J. H., & Abel, T. 2007, ApJ, 671, 1559 10.1086/522876 2007ApJ...671.1559W Google Scholar
Wise, J. H., & Abel, T. 2008, ApJ, 685, 40 10.1086/590417 2008ApJ...685...40W Google Scholar
Wise, J. H., Turk, M. J., & Abel, T. 2008, ApJ, 682, 745 10.1086/588209 2008ApJ...682..745W Google Scholar
Wolcott-Green, J., Haiman, Z., & Bryan, G. L. 2011, MNRAS, 418, 838 10.1111/j.1365-2966.2011.19538.x 2011MNRAS.418..838W Google Scholar
Wolcott-Green, J., Haiman, Z., & Bryan, G. L. 2017, MNRAS, 469, 3329 10.1093/mnras/stx167 2017MNRAS.469.3329W Google Scholar
Woods, T. E., Heger, A., Whalen, D. J., Haemmerle, L., & Klessen, R. S. 2017, ApJL, 842, 6 Google Scholar
Wu, X.-B., et al. 2015, Nature, 518, 512 10.1038/nature14241 2015Natur.518..512W Google Scholar
Yajima, H., & Khochfar, S. 2016, MNRAS, 457, 2423 10.1093/mnras/stw058 2016MNRAS.457.2423Y Google Scholar
Yoo, J., & Miralda-Escudé, J. 2004, ApJ, 614, L25 10.1086/425416 2004ApJ...614L..25Y Google Scholar
Yoshida, N., Omukai, K., & Hernquist, L. 2008, Science, 321, 669 10.1126/science.1160259 2008Sci...321..669Y Google Scholar
Yue, B., Ferrara, A., Salvaterra, R., Xu, Y., & Chen, X. 2014, MNRAS, 440, 1263 10.1093/mnras/stu351 2014MNRAS.440.1263Y Google Scholar
Zhang, J., Fakhouri, O., & Ma, C.-P. 2008, MNRAS, 389, 1521 10.1111/j.1365-2966.2008.13671.x 2008MNRAS.389.1521Z Google Scholar
Zheng, W., et al. 2006, ApJ, 640, 574 10.1086/500167 2006ApJ...640..574Z Google Scholar
Zinn, P.-C., Middelberg, E., Norris, R. P., & Dettmar, R.-J. 2013, ApJ, 774, 66 10.1088/0004-637X/774/1/66 2013ApJ...774...66Z Google Scholar
Zubovas, K., & King, A. 2012, ApJ, 745, L34 10.1088/2041-8205/745/2/L34 2012ApJ...745L..34Z Google Scholar
Figure 0

Figure 1. Left panel: Black hole mass as a function of redshift in z > 3 galaxies. References to the data are labelled and colour coded in the figure. Right panel: Bolometric luminosity as a function of the black hole mass for z ~ 6 quasars. Blue square, red circle, and magenta triangle represent quasars J0100, J1148, and J1120, respectively. Black empty data points are from the collection of high-z quasars by Wu et al. (2015). The green dashed line shows Eddington luminosity (Courtesy of F. Wang and X.B. Wu).

Figure 1

Figure 2. The McritJLW relation from O’Shea & Norman (2008), Figure 3. The squares represent their updated calculations while the Machacek et al. (2001) relation is depicted by the dashed line. The empty square represent the case with JLW = 0. If the mass of a pristine minihalo exposed to a given JLW, lies above the curve formed by the squares, it is considered Pop III star forming.

Figure 2

Figure 3. From Agarwal et al. (2012): The background and local level of LW radiation plotted for each redshift. ‘The red triangles (JIIlocal) and blue crosses (JIIIlocal) indicate the maximum value of LW radiation to which a pristine halo is exposed at each redshift in their volume. The red and blue dashed lines represent JIIcrit and JIIIcrit respectively. It is interesting to see that the maximum value of JIIIlocal (blue crosses) falls short of JIIIcrit (blue dashed line). However, in the case of Pop II sources, the maximum value of JIIlocal (red triangles) is several orders of magnitude higher than the JIIcrit (red dashed line). The green dotted line is the specific intensity Jbg given by Dijkstra et al. (2014). Finally, the yellow dotted line shows the average LW emission from Valiante et al. (2016).

Figure 3

Figure 4. Co-moving number density of halos that can host a DCBH, at a given redshift. Symbols represent different radiation intensity thresholds. Squares: JLW, crit = 30, circles: JLW, crit = 100, triangles: JLW, crit = 300. The horizontal solid blue line shows the co-moving number density of z ~ 6 SMBHs. The light gray crossed square at z = 10.5 is from the hydrodynamical simulation by Agarwal et al. (2014), the light gray squares in the range z = 10–7 are from Agarwal et al. (2012, private communication), dark gray squares and black triangles are the results of Dijkstra et al. (2014) and Valiante et al. (2016), respectively. The orange square shows the number density for Habouzit et al. (2016c) (10 cMpc side box, tff, see text). The purple squares and circles show the number density for Habouzit et al. (2016c) (10 cMpc side box, 10 Myr, see text). The cyan squares, circle, and triangle represent the large-scale cosmological simulation Horizon-noAGN (Dubois et al. 2014b; Habouzit et al. 2016c, 142 cMpc side box).

Figure 4

Figure 5. Distribution of local radiation intensity (Agarwal et al. 2012) seen by pristine halos at z = 16 (top panel), before the formation of Pop II begins, and later on at z ~ 9 (bottom panel) when Pop II is already in place. fpris is the number fraction of pristine halos exposed to a given JLW. Radiation intensity from Pop III stars is shown in red, and radiation intensity from Pop II stars in blue. Dashed lines indicate the critical radiation intensity expected for Pop III stars (in red) and Pop II stars (in blue). Pop III stars radiation intensity appears to be almost always below the critical intensity (below the corresponding dashed line), whereas a fraction of pristine halo illuminated by Pop II stars radiation flux can meet the critical radiation intensity condition.

Figure 5

Figure 6. Distribution of the average number of seed BHs as function of the DM halo mass from different seeding prescriptions adopted in pSAMs: (i) equal-mass 100 M low-mass seeds (left panel) and (ii) (10–140) and (260–300) M Pop III remnant BHs (middle panel) plus 105 M high-mass seeds (right panel). Histograms and data points show the number of total (in lighter colours) and real SMBH progenitors (darker histograms, see text). Error bars account for the 1σ dispersion. The figures are adapted from Pezzulli et al. (2016) and Valiante et al. (2016). The average redshift range in which seeds form, according to these two models, is given in each panel.

Figure 6

Figure 7. The growth of a low-mass seed BH mass as a function of redshift in different regimes: Eddington-limited gas accretion with radiative efficiencies ε = 0.1, 0.2, 0.4 (solid, short-dashed and dot-dashed lines, respectively); super-critical accretion (long-dashed line). The figure is taken from Volonteri & Rees (2006b).

Figure 7

Figure 8. The cosmic cycle of a typical quasars at z ~ 6. Models reproduce the properties of J1148 (see text). Left panel: the build-up of the MBHMstar relation through cosmic time as compared with data and empirical fit for local galaxies (Sani et al. 2011). Middle panel: the predicted star formation history via quiescent and merger-driven bursts (see e.g. Valiante et al. 2011). Left panel: the assembly of the dust mass into the ISM as a function of the stellar mass. In all panels, the solid lines show the average over 50 different DM halo merger trees with shades representing the 1σ dispersion. These figures are adapted from Valiante et al. (2011).