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The Correlation Between Galaxy Growth and Dark Matter Halo Assembly from Z = 0 − 10

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The Correlation Between Galaxy Growth and Dark Matter Halo Assembly from Z = 0 − 10 MNRAS 000, 000–000 (2019) Preprint 30 July 2019 Compiled using MNRAS LATEX style file v3.0 UNIVERSEMACHINE: The Correlation between Galaxy Growth and Dark Matter Halo Assembly from z = 0 − 10 Peter Behroozi1,? Risa H. Wechsler2;3, Andrew P. Hearin4, Charlie Conroy5 1 Department of Astronomy and Steward Observatory, University of Arizona, Tucson, AZ 85721, USA 2 Kavli Institute for Particle Astrophysics and Cosmology and Department of Physics, Stanford University, Stanford, CA 94305, USA 3 Department of Particle Physics and Astrophysics, SLAC National Accelerator Laboratory, Stanford, CA 94305, USA 4 High-Energy Physics Division, Argonne National Laboratory, Argonne, IL 60439, USA 5 Department of Astronomy, Harvard University, Cambridge, MA 02138, USA Released 30 July 2019 ABSTRACT We present a method to flexibly and self-consistently determine individual galaxies’ star for- mation rates (SFRs) from their host haloes’ potential well depths, assembly histories, and red- shifts. The method is constrained by galaxies’ observed stellar mass functions, SFRs (specific and cosmic), quenched fractions, UV luminosity functions, UV–stellar mass relations, IRX– UV relations, auto- and cross-correlation functions (including quenched and star-forming sub- samples), and quenching dependence on environment; each observable is reproduced over the full redshift range available, up to 0 < z < 10. Key findings include: galaxy assembly corre- lates strongly with halo assembly; quenching correlates strongly with halo mass; quenched fractions at fixed halo mass decrease with increasing redshift; massive quenched galaxies re- side in higher-mass haloes than star-forming galaxies at fixed galaxy mass; star-forming and quenched galaxies’ star formation histories at fixed mass differ most at z < 0:5; satellites have large scatter in quenching timescales after infall, and have modestly higher quenched frac- tions than central galaxies; Planck cosmologies result in up to 0:3 dex lower stellar—halo mass ratios at early times; and, nonetheless, stellar mass–halo mass ratios rise at z > 5. Also presented are revised stellar mass—halo mass relations for all, quenched, star-forming, cen- tral, and satellite galaxies; the dependence of star formation histories on halo mass, stellar mass, and galaxy SSFR; quenched fractions and quenching timescale distributions for satel- lites; and predictions for higher-redshift galaxy correlation functions and weak lensing surface densities. The public data release (DR1) includes the massively parallel (> 105 cores) im- plementation (the UNIVERSEMACHINE), the newly compiled and remeasured observational data, derived galaxy formation constraints, and mock catalogues including lightcones. Key words: galaxies: formation; galaxies: haloes 1 INTRODUCTION semi-analytic models, which use known physics as a strong prior arXiv:1806.07893v2 [astro-ph.GA] 26 Jul 2019 on how galaxies may form. For example, current implementations In LCDM cosmologies, galaxies form at the centres of gravitation- attempt to simulate the effects of supernovae, radiation pressure, ally self-bound, virialized dark matter structures (known as haloes). multiphase gas, black hole accretion, photo- and collisional ion- Haloes form hierarchically, and the largest collapsed structure in a ization, and chemistry (see Somerville & Davé 2015; Naab & Os- given overdensity (i.e., a central halo) can contain many smaller triker 2017, for reviews). All such methods approximate physics self-bound structures (satellite haloes). While the broad contours below their respective resolution scales (galaxies for semi-analytic of galaxy formation physics are known (see Silk & Mamon 2012; models; particles and/or grid elements for hydrodynamical simu- Somerville & Davé 2015, for reviews), a fully predictive frame- lations), and different reasonable approximations lead to different work from first principles does not yet exist (see Naab & Ostriker resulting galaxy properties (Lu et al. 2014b; Kim et al. 2016). 2017, for a review). Traditional theoretical methods include hydrodynamical and These methods are complemented by empirical modeling, wherein the priors are significantly weakened and the physical con- straints come almost entirely from observations. Current empiri- ? E-mail: [email protected] cal models constrain physics averaged over galaxy scales, simi- c 2019 The Authors 2 Behroozi, Wechsler, Hearin, Conroy lar to semi-analytical models. Indeed, as empirical modeling has Empirical models that correlate galaxy star formation rates grown in complexity and self-consistency, as well as in the number or colours with halo concentrations (correlated with halo forma- of galaxy (e.g., Moster et al. 2018; Rodríguez-Puebla et al. 2017; tion time; Wechsler et al. 2002) have shown success in matching Somerville et al. 2018), gas (e.g., Popping et al. 2015), metallicity galaxy autocorrelation functions, weak lensing, and radial profiles (e.g., Rodríguez-Puebla et al. 2016b), and dust (Imara et al. 2018) of quenched galaxy fractions around clusters (Hearin & Watson observables generated, the mechanics of semi-analytic and empir- 2013; Hearin et al. 2014; Watson et al. 2015). Models that re- ical models have become increasingly similar. For example, the late galaxy SFRs linearly to halo mass accretion rates (albeit non- techniques of post-processing merger trees from N-body simula- linearly to halo mass; Taghizadeh-Popp et al. 2015; Becker 2015; tions, comparing to galaxy correlation functions, and using orphan Rodríguez-Puebla et al. 2016a; Sun & Furlanetto 2016; Mitra et al. galaxies were commonplace in semi-analytical models well before 2017; Cohn 2017; Moster et al. 2018) have also shown success in they were used in empirical ones (Roukema et al. 1997; Kauffmann this regard. To date, all such models have made a strong assump- et al. 1999). tion that galaxy formation is perfectly correlated to a chosen proxy Nonetheless, the presence or absence of strong physical pri- for halo assembly. ors remains a key difference between empirical and semi-analytic In our approach, we do not impose an a priori correlation models. While semi-analytic models can therefore obtain tighter between galaxy assembly and halo assembly. Instead, given that parameter constraints for the same data (or lack thereof), empirical galaxy clustering depends strongly on this correlation, we can di- models can reveal physics that was not previously expected to exist rectly measure it. Our method first involves making a guess for how (e.g., Behroozi et al. 2013c; Behroozi & Silk 2015). In cases where galaxy SFRs depend on host halo potential well depth, assembly traditional methods have strong disagreements (e.g., on the mecha- history, and redshift. This ansatz is then self-consistently applied nism for galaxy quenching), this latter quality can be very powerful, to halo merger trees from a dark matter simulation, resulting in a and is hence a strong motivation for using empirical modeling here. mock universe; this mock universe is compared directly with real Most current empirical models relate galaxy properties to observations to compute a Bayesian likelihood. A Markov Chain properties of their host dark matter haloes. Larger haloes host larger Monte Carlo algorithm then makes a new guess for the galaxy SFR galaxies, with relatively tight scatter in the stellar mass—halo mass function, and the process is repeated until the range of SFR func- relation (More et al. 2009; Yang et al. 2009; Leauthaud et al. 2012; tions that are compatible with observations is fully sampled. Reddick et al. 2013; Watson & Conroy 2013; Tinker et al. 2013; Observational constraints used here include stellar mass func- Gu et al. 2016). Hence, it has become common to investigate aver- tions, UV luminosity functions, the UV–stellar mass relation, spe- age galaxy growth via a connection to the average growth of haloes cific and cosmic SFRs, galaxy quenched fractions, galaxy autocor- (Zheng et al. 2007; White et al. 2007; Conroy & Wechsler 2009; relation functions, and the quenched fraction of central galaxies as Firmani & Avila-Reese 2010; Leitner 2012; Béthermin et al. 2013; a function of environmental density. We also compare to galaxy- Wang et al. 2013; Moster et al. 2013; Behroozi et al. 2013e,f; Mutch galaxy weak lensing. High-redshift constraints have improved dra- et al. 2013; Birrer et al. 2014; Marchesini et al. 2014; Lu et al. matically in the past five years due to the CANDELS, 3D-HST, 2014a, 2015b; Papovich et al. 2015; Li et al. 2016; see Wechsler ULTRAVISTA, and ZFOURGE surveys (Grogin et al. 2011; Bram- & Tinker 2018 for a review). These studies have found that the mer et al. 2012; McCracken et al. 2012). At the same time, pipeline stellar mass—halo mass relation is relatively constant with redshift and fitting improvements have made significant changes to the in- from 0 < z < 4 (Behroozi et al. 2013c), but may evolve signifi- ferred stellar masses of massive low-redshift galaxies (Bernardi cantly at z > 4 (Behroozi & Silk 2015; Finkelstein et al. 2015b; et al. 2013). As with past analyses (Behroozi et al. 2010, 2013e), Sun & Furlanetto 2016). we marginalize over many systematic uncertainties, including those If galaxy mass is tightly correlated with halo mass on aver- from stellar population synthesis, dust, and star formation history age, it is natural to expect that individual galaxy assembly could models. be correlated with halo assembly. This assembly correlation for in- dividual galaxies has strong observational support. For
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