Astronomy has changed from data-starving science to data-flooding science about 20 years ago due to advances of observational technology in all the wavelength regimes of electromagnetic waves. This paper gives a historical overview of galaxy surveys. We start from the impact of the technology development. Then, old imaging surveys and redshift surveys based on photography, especially using Schmidt telescopes, in the era of data-starving science are described in some detail. Several features of modern surveys are given and two highlights obtained from the exploitation of modern galaxy surveys are introduced.

Thanks to deep optical to near-IR imaging and spectroscopy, significant progress is made in characterizing the rest-frame UV to optical properties of galaxies in the early universe (z > 4. Surveys with Hubble, Spitzer, and ground-based facilities (Keck, Subaru, and VLT) provide spectroscopic and photometric redshifts, measurements of the spatial structure, stellar masses, and optical emission lines for large samples of galaxies. Recently, the Atacama Large (Sub) Millimeter Array (ALMA) has become a major player in pushing studies of high redshift galaxies to far-infrared wavelengths, hence making panchromatic surveys over many orders of frequencies possible. While past studies focused mostly on bright sub-millimeter galaxies, the sensitivity of ALMA now enables surveys like ALPINE, which focuses on measuring the gas and dust properties of a large sample of normal main-sequence galaxies at z > 4. Combining observations across different wavelengths into a single, panchromatic picture of galaxy formation and evolution is currently and in the future an important focus of the astronomical community.

One of the key questions of the observational cosmology is how the environmental dependence of galaxies today formed. Proto-clusters, galaxy overdense regions at high redshift are important laboratory to study the formation history of clusters of galaxies. We perform the first statistic study of far-infrared spectral energy distribution(SED)s of proto-clusters at z ∼ 4 by the stacking analysis of Planck/ AKARI/ IRAS images of proto-clusters at z ∼ 4 selected from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. By stacking ∼ 200 proto-clusters, we successfully constrain their average total SEDs in 60–850μm. Our results imply the excess of dusty starburst galaxies with star formation rate several 1000 M⊙yr−1 in total and obscured AGNs in proto-clusters at z ∼ 4.

We present ongoing work on the spectral energy distributions (SEDs) of active galactic nuclei (AGNs), derived from X-ray, ultraviolet, optical, infrared and radio photometry and spectroscopy. Our work is motivated by new wide-field imaging surveys that will identify vast numbers of AGNs, and by the need to benchmark AGN SED fitting codes. We have constructed 41 SEDs of individual AGNs and 80 additional SEDs that mimic Seyfert spectra. All of our SEDs span 0.09 to 30μm, while some extend into the X-ray and/or radio. We have tested the utility of the SEDs by using them to generate AGN photometric redshifts, and they outperform SEDs from the prior literature, including reduced redshift errors and flux density residuals.

Modelling and interpreting the SEDs of galaxies has become one of the key tools at the disposal of extragalactic astronomers. Ideally, we could hope that, through a detailed study of its SED, we can infer the correct physical properties and the evolutionary history of a galaxy. In the past decade, panchromatic SED fitting, i.e. modelling the SED over the entire UV–submm wavelength regime, has seen an enormous advance. Several advanced new codes have been developed, nearly all based on Bayesian inference modelling. In this review, we briefly touch upon the different ingredients necessary for panchromatic SED modelling, and discuss the methodology and some important aspects of Bayesian SED modelling. The current uncertainties and limitations of panchromatic SED modelling are discussed, and we explore some avenues how the models and techniques can potentially be improved in the near future.

By considering a modified version of the evolutionary population synthesis (EPS) model for stellar populations (SPs) comprising binary stars, the retrieved galaxy and HII-region parameters/properties differ from the case of neglecting binary stars. The retrieved age, stellar metallicity and mass of galaxies increase (e.g. ∼ 0.2 dex when using spectral fitting algorithm), whilst the star formation rate decreases (∼0.2 dex). The radiation fields from intermediate-age SPs with binary stars could be potentially important ionizing sources in HII regions. Under this possibility, the theoretical division between star forming galaxy and AGN on the diagnostic diagrams would move towards the up-right corner and the retrieved gaseous metallicity would decrease.

Our prediction for the birth rate of binary neutron stars in SPs ranges from 10−9 to 10−6${\M {^\minus 1_\odot}} $ yr−1 when the kick velocity is from 0 to 190 km s−1.

The Herschel Extragalactic Legacy Project (HELP) focuses to publish an astronomical multiwavelength catalogue of millions of objects over 1300 deg2 of the Herschel Space Observatory survey fields. Millions of galaxies with ultraviolet–far infrared photometry make HELP a perfect sample for testing spectral energy distribution fitting models, and to prepare tools for next-generation data. In the frame of HELP collaboration we estimated the main physical properties of all galaxies from the HELP database and we checked a new procedure to select peculiar galaxies from large galaxy sample and we investigated the influence of used modules for stellar mass estimation.

Early-type galaxies (ETGs) are of crucial importance to trace back the galaxy mass assembly across cosmic time, yet their formation and quenching remain remarkably elusive. The discoveries of massive, dead galaxies at ever-growing redshifts provided compelling evidence to push their formation up to redshift > 4–5 when the Universe was barely 1 Gyr old. In this talk I will present our results on the ages of a new sample of ETGs at z ∼ 3, built by exploiting HST WFC3/G141 rest-frame optical/near-UV grism spectroscopy to study the nature of 10 passive galaxy candidates at 2.5 < z < 3.5 in COSMOS.

This work is part of a PhD project aimed at quantifying the parent space density of distant genuine passive galaxies. I will also discuss the importance of multi-wavelength data in clarifying the degree of contamination by dusty star-forming galaxies affecting the color selection.

We study star formation and metallicity enrichment histories of 24 massive galaxies at 1.6 < z < 2.5. Deep slitless spectroscopy + imaging data set collected from multiple HST surveys allows robust determination of their SEDs. Our new SED modeling with no functional assumptions on star formation histories revels that 1. most of the sample galaxies have already formed >50% of their extant masses ∼1.5 Gyr before the time of observed redshifts, with a trend where more massive galaxies form earlier, 2. most of our galaxies already have stellar metallicities compatible with those of local early-type galaxies, and 3. inferred metallicities are on average ∼ 0.25 dex higher than observed gas-phase metallicities of star forming galaxies at the time of their formation. Continuation of low-level star formation, rather than abrupt termination of star forming activity, may explain the observed gap of metallicities.

Despite decreasing cosmic star formation rate density over the last 10 Gyr, the stellar mass (M*) buildups in galaxies were still progressing during this epoch. About 50% of the current M* density in the universe was built over the last ∼8.7 Gyr. In this research, we investigated the stellar mass buildup and quenching of spatially resolved regions within massive disk galaxies over the last 10 Gyr. We apply the spectral energy distribution (SED) fitting method to SEDs of sub-galactic regions in galaxies to derive the spatially resolved distributions of SFR and M* in the galaxies. This namely pixel-to-pixel SED fitting method is applied to massive disk galaxies at 0.01 < z < 0.02 and 0.8 < z < 1.8. We found that massive disk galaxies tend to build their M* and quench their star formation progressively from the central region to the outskirts, i.e. inside-out stellar mass buildup and quenching.

The star formation history (SFH) of galaxies allow us to investigate when galaxies formed their stars and assembled their mass. We can constrain the SFH with high level of precision from galaxies with resolved stellar populations, since we are able to discriminate between stars of different ages from the spectrum they emit. However, the relative importance of secular evolution (nature) over nurture is not yet clear, and separating the effects of interaction-driven evolution in the observed galaxy properties is not trivial. The aim of this study is to use MaNGA (Mapping Nearby Galaxies at APO) Integral Field Unit (IFU) data, in combination with multi-wavelength data, to constrain the SFH of nearby isolated galaxies. We present here the new techniques we are developing to constrain the SFH with high level of precision from Spectral Energy Distribution (SED) fitting. This study is part of a China-Chile collaboration program where we are applying these new techniques to investigate how galaxies formed and evolve in different environments.

Dust radiative transfer simulations provide us with the unique opportunity to study the heating mechanisms of dust by the stellar radiation field. From 2D observational images we derive the 3D distributions of stars and dust. Our aim is to analyze the contribution of the different stellar populations to the radiative dust heating processes in the nearby face-on barred galaxies NGC 1365, M 83 and M 95. We wish to decompose the FIR-submm SED and quantify the fraction directly related to star formation. To model the complex geometries mentioned above, we used SKIRT, a state-of-the-art, 3D Monte Carlo radiative transfer code designed to simulate the absorption, scattering and thermal re-emission of dust in a variety of environments. We find that the contribution of the evolved stars (8 Gyr) to the dust heating is non-negligible (∼35%) and can reach as high as 70%. We also find a tight correlation between the heating fraction by the unevolved stars (⩽ 100 Myr) and the specific star formation rate.

Dust attenuation shapes the spectral energy distributions of galaxies and any modelling and fitting procedure of their spectral energy distributions must account for this process. We present results of two recent works dedicated at measuring the dust attenuation curves in star forming galaxies at redshift from 0.5 to 3, by fitting continuum (photometric) and line (spectroscopic) measurements simultaneously with CIGALE using variable attenuation laws based on flexible recipes. Both studies conclude to a large variety of effective attenuation laws with an attenuation law flattening when the obscuration increases. An extra attenuation is found for nebular lines. The comparison with radiative transfer models implies a flattening of the attenuation law up to near infrared wavelengths, which is well reproduced with a power-laws recipe inspired by the Charlot and Fall recipe. Here we propose a global modification of the Calzetti attenuation law to better reproduce the results of radiative transfer models.

Stellar masses are crucial ingredients for putting galaxies in the context of galaxy evolution and are commonly evaluated via Spectral Energy Distribution (SED)-fitting analyses which are hampered by dust attenuation. Observational constraints of attenuation in various galaxy classes provide key inputs for fitting a SED. I will present recent results about the attenuation properties of a sample of Herschel-selected galaxies at 0.7 ⩽ z ⩽ 1.6 widely spanning the star-forming Main Sequence (MS). I will show that far-IR selected galaxies on the MS are well described with local attenuation recipes. Conversely, common recipes cannot recover the SFR of far-IR selected starburst galaxies well above the MS. The SFR of these outliers appears to be hidden by the ∼90% in optically thick cores. These findings pose challenges for SED-fitting codes based on energy balance assumptions that might break in these peculiar sources.

We analyse the dust-to-gas mass ratio (DGR) in nearby galaxies on kiloparsec scales. We focus on their dependence on metallicity and the CO-to-H2 conversion factor, αco. We use a sample of 25 nearby galaxies from SINGS and combine our data with CO (2-1) and H I observations from the HERACLES and THINGS surveys. We implement a Hierarchical Bayesian method to derive the dust mass via fitting the infrared data from 100 to 500 μm with a single modified blackbody. We find that the DGR-metallicity relation follows a power law and we study its strong dependency on the conversion factor αco. Our results indicate a strong connection between interstellar dust and gas. The resolved DGR-metallicity relation cannot be represented with a single power law. The scatter in this relation shows the strong impact of several processes that take place in every galaxy.

Brightest cluster galaxies (BCGs) residing in cool-core clusters are known to be the stage of intricate baryon cycle phenomena (e.g. gas inflows, AGN outflows, star formation feedback). The scenarios describing the observed properties of these galaxies are still controversial, suffering from limitations due to the spatial resolving power of the instruments, specifically for galaxies beyond the Local Universe. However, the dramatic improvements introduced by the integral-field unit instruments (e.g. MUSE) could shed light on the physical processes driving the evolution of these galaxies. We present an extensive analysis of the stellar and gas properties (i.e. kinematics, stellar mass, star formation rate) of the radio-loud BCG sitting at the centre of the X-ray luminous cool-core cluster Abell 2667 (z = 0.23), based on MUSE data. Our results indicate that the BCG is a massive elliptical, hosting an AGN that is possibly undergoing accretion of cold star-forming clouds of ICM or galactic cannibalism.

This paper summarizes my thoughts, given in an invited review at the IAU symposium 341 “Challenges in Panchromatical Galaxy Modelling with Next Generation Facilities”, about how machine learning methods can help us solve some of the big data problems associated with current and upcoming large galaxy surveys.

LSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim.

Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.

In this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.