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The Milky Way’s total satellite population and constraining the mass of the warm dark matter particle

Published online by Cambridge University Press:  30 October 2019

Oliver Newton Open the ORCID record for Oliver Newton [Opens in a new window] ,
Marius Cautun ,
Adrian Jenkins ,
Carlos S. Frenk  and
John C. Helly
Oliver Newton*
Affiliation:
Institute for Computational Cosmology, Durham University, Durham, DH1 3LE, UK
Marius Cautun
Affiliation:
Institute for Computational Cosmology, Durham University, Durham, DH1 3LE, UK
Adrian Jenkins
Affiliation:
Institute for Computational Cosmology, Durham University, Durham, DH1 3LE, UK
Carlos S. Frenk
Affiliation:
Institute for Computational Cosmology, Durham University, Durham, DH1 3LE, UK
John C. Helly
Affiliation:
Institute for Computational Cosmology, Durham University, Durham, DH1 3LE, UK
*
†email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

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The Milky Way’s (MW) satellite population is a powerful probe of warm dark matter (WDM) models as the abundance of small substructures is very sensitive to the properties of the WDM particle. However, only a partial census of the MW’s complement of satellite galaxies exists because surveys of the MW’s close environs are incomplete both in depth and in sky coverage. We present a new Bayesian analysis that combines the sample of satellites recently discovered by the Dark Energy Survey (DES) with those found in the Sloan Digital Sky Survey (SDSS) to estimate the total satellite galaxy luminosity function down to Mv = 0. We find that there should be at least $124_{ - 27}^{ + 40}$ (68% CL, statistical error) satellites as bright or brighter than Mv = 0 within 300 kpc of the Sun, with only a weak dependence on MW halo mass. When it comes online the Large Synoptic Survey Telescope should detect approximately half of this population. We also show that WDM models infer the same number of satellites as in ΛCDM, which will allow us to rule out those models that produce insufficient substructure to be viable.

Keywords

Galaxy: halogalaxies: dwarfdark matter
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S344: Dwarf Galaxies: From the Deep Universe to the Present , August 2018 , pp. 109 - 113
Copyright
© International Astronomical Union 2019 

References

Alam, S., et al, 2015, ApJS, 219, 12 CrossRefGoogle Scholar
Avila-Reese, V., Coln, P., Valenzuela, O., D’Onghia, E., & Firmani, C., 2001, ApJ, 559, 516 CrossRefGoogle Scholar
Bechtol, K., et al, 2015, ApJ, 807, 50 CrossRefGoogle Scholar
Bode, P., Ostriker, J. P., & Turok, N., 2001, ApJ, 556, 93 CrossRefGoogle Scholar
Bose, S., et al, 2016, MNRAS, 455, 318 CrossRefGoogle Scholar
Bose, S., et al, 2017, MNRAS, 464, 4520 CrossRefGoogle Scholar
Bose, S., Deason, A. J., & Frenk, C. S., 2018, ApJ, 863, 123 CrossRefGoogle Scholar
Drlica-Wagner, A., et al, 2015, ApJ, 813, 109 CrossRefGoogle Scholar
Fattahi, A., et al, 2016, MNRAS, 457, 844 CrossRefGoogle Scholar
Hargis, J. R., Willman, B., & Peter, A. H. G., 2014, ApJ, 795, L13 CrossRefGoogle Scholar
Hellwing, W. A., et al, 2016, MNRAS, 457, 3492 CrossRefGoogle Scholar
Kennedy, R., Frenk, C., Cole, S., & Benson, A., 2014, MNRAS, 442, 2487 CrossRefGoogle Scholar
Koposov, S., et al, 2008, ApJ, 686, 279 CrossRefGoogle Scholar
Lovell, M. R., et al, 2012, MNRAS, 420, 2318 CrossRefGoogle Scholar
Lovell, M. R., et al, 2014, MNRAS, 439, 300 CrossRefGoogle Scholar
Lovell, M. R., et al, 2017, MNRAS, 468, 4285 CrossRefGoogle Scholar
Macciò, A. V., & Fontanot, F., 2010, MNRAS, 404, L16 CrossRefGoogle Scholar
Newton, O., Cautun, M., Jenkins, A., Frenk, C. S., & Helly, J. C., 2018, MNRAS, 479, 2853 CrossRefGoogle Scholar
Peebles, P. J. E., 1982, ApJ, 263, L1 CrossRefGoogle Scholar
Polisensky, E., & Ricotti, M., 2011, Phys. Rev. D, 83, 043506 CrossRefGoogle Scholar
Sawala, T., et al, 2016, MNRAS, 457, 1931 CrossRefGoogle Scholar
Schewtschenko, J. A., et al, 2015, MNRAS, 449, 3587 CrossRefGoogle Scholar
Schneider, A., 2016, J. Cosmol. Astropart. Phys., 2016, 059 CrossRefGoogle Scholar
Springel, V., et al, 2008, MNRAS, 391, 1685 CrossRefGoogle Scholar
Tollerud, E. J., Bullock, J. S., Strigari, L. E., & Willman, B., 2008, ApJ, 688, 277 CrossRefGoogle Scholar