Exploring the high-mass end of the stellar mass function of star-forming galaxies at cosmic noon

Abstract

We present the high-mass end of the galaxy stellar mass function using the largest sample to date (5,352) of star-forming galaxies with M > 10 11 M at cosmic noon, 1.5 < z < 3.5. This sample is uniformly selected across 17.2 deg 2 (∼0.44 Gpc 3 comoving volume from 1.5 < z < 3.5), mitigating the effects of cosmic variance and encompassing a wide range of environments. This area, a factor of 10 larger than previous studies, provides robust statistics at the high-mass end. Using multi-wavelength data in the Spitzer /HETDEX Exploratory Large Area (SHELA) footprint we find that the SHELA footprint star-forming galaxy stellar mass function is steeply declining at the high-mass end probing values as high as ∼10 −4 Mpc 3 /dex and as low as ∼5×10 −8 Mpc 3 /dex across a stellar mass range of log(M /M ) ∼ 11 -12. We compare our empirical star-forming galaxy stellar mass function at the high mass end to three types of numerical models: hydrodynamical models from IllustrisTNG, abundance matching from the UniverseMachine, and three different semi-analytic models (SAMs; SAG, SAGE, GALACTICUS). At redshifts 1.5 < z < 3.5 we find that results from Illus-trisTNG and abundance matching models agree within a factor of ∼2 to 10, however the three SAMs strongly underestimate (up to a factor of 1,000) the number density of massive galaxies. We discuss the implications of these results for our understanding of galaxy evolution.

Investigating the growing population of massive quiescent galaxies at cosmic noon

“…We note that the sample used in this paper is constructed in a similar way to that from Sherman et al (2020), however one key difference has allowed for the study contained in this work. Sherman et al (2020) performed a study of massive ( ★ ≥ 10 11 M ) star-forming galaxies in the same footprint used here, but their analysis was limited to star-forming galaxies, as the catalog used (Wold et al 2019) was riz-selected. Since that publication, NEWFIRM data have become available in the footrprint, which allows for the selection of both the star-forming and quiescent populations of massive galaxies.…”

“…We utilize the default set of EAZY-py FSPS templates which are constructed using a Chabrier (2003) initial mass function (IMF), Kriek & Conroy (2013) dust law, solar metallicity, and realistic star-formation histories including bursty and slowly rising models. A full description of the fitting procedure used here and tests of EAZY-py on a set of mock galaxies are given in Sherman et al (2020) and will briefly be described here.…”

“…We first use the built-in EAZY-py fitting procedure to find the best fit template combination at the redshift at which 2 is minimized. Then, at this redshift we draw 100 SEDs from the best-fit SED's template error distribution (Sherman et al 2020). Each of these SED draws gives a unique set of galaxy parameters such as stellar mass and SFR, and we construct distributions of these parameters from the 100 draws.…”

“…To validate this SED fitting procedure for redshift and parameter recovery, Sherman et al (2020) performed extensive tests of EAZYpy by fitting a diverse sample of mock galaxies (V. Acquaviva, private communication). These mock galaxies were assigned realistic error bars using the SHELA footprint sample of galaxies.…”

“…Eddington bias describes the situation where uncertainties in both photometry and photometric redshifts can cause low-mass galaxies to scatter into high-mass bins. In our test (see also Sherman et al 2020), we generate 100 iterations of our catalog by drawing 100 new photometric redshifts for each galaxy from that galaxy's photometric redshift probability distribution. We then re-fit galaxies at their drawn redshifts using the SED fitting procedure described above and perform the sample selection and analysis presented in Sections 3.1 and 4 of this work.…”