Subhalo Abundance Matching Research Articles

SHAMe-SF: Predicting the clustering of star-forming galaxies with an enhanced abundance matching model

Context. With the advent of several galaxy surveys targeting star-forming galaxies, it is important to have models capable of interpreting their spatial distribution in terms of astrophysical and cosmological parameters. Aims. We introduce SHAMe-SF, an extension of the subhalo abundance matching (SHAM) technique designed specifically for analysing the redshift-space clustering of star-forming galaxies. Methods. Our model directly links a galaxy’s star-formation rate to the properties of its host dark matter subhalo, with further modulations based on effective models of feedback and gas stripping. To quantify the accuracy of our model, we show that it simultaneously reproduces key clustering statistics such as the projected correlation function, monopole, and quadrupole of star-forming galaxy samples at various redshifts and number densities. These tests were conducted over a wide range of scales [0.6, 30] h−1 Mpc using samples from both the TNG300 magneto-hydrodynamic simulation and a semi-analytical model. Results. SHAMe-SF can reproduce the clustering of simulated galaxies selected by star-formation rate as well as galaxies that fall within the colour selection criteria employed by DESI for emission line galaxies. Conclusions. Our model exhibits several potential applications, including the generation of covariance matrices, exploration of galaxy formation processes, and even placing constraints on the cosmological parameters of the Universe.

Characterizing ultra-high-redshift dark matter halo demographics and assembly histories with the gureft simulations

ABSTRACT Dark matter halo demographics and assembly histories are a manifestation of cosmological structure formation and have profound implications for the formation and evolution of galaxies. In particular, merger trees provide fundamental input for several modelling techniques, such as semi-analytic models (SAMs), sub-halo abundance matching (SHAM), and decorated halo occupation distribution models. Motivated by the new ultra-high-redshift (z ≳ 10) frontier enabled by JWST, we present a new suite of Gadget at Ultrahigh Redshift with Extra-Fine Timesteps (gureft) dark matter-only cosmological simulations that are carefully designed to capture halo merger histories and structural properties in the ultra-z universe. The simulation suite consists of four 10243-particle simulations with box sizes of 5, 15, 35, and 90 Mpc h−1, each with 170 snapshots stored between 40 ≥ z ≥ 6. With the unprecedented number of available snapshots and strategically chosen dynamic range covered by these boxes, gureft uncovers the emerging dark matter halo populations and their assembly histories in the earliest epochs of cosmic history. In this work, we present the halo mass functions (HMF) between z ∼ 20 and 6 down to log (Mvir/M⊙) ∼ 5, and show that at high redshift, these robust HMFs can differ substantially from commonly used analytic approximations or older fitting functions in the literature. We also present key physical properties of the ultra-high z halo population, such as concentration and spin, as well as their mass growth and merger rates, and again provide updated fitting functions.

Fast forward modelling of galaxy spatial and statistical distributions

A forward modelling approach provides simple, fast and realistic simulations of galaxy surveys, without a complex underlying model. For this purpose, galaxy clustering needs to be simulated accurately, both for the usage of clustering as its own probe and to control systematics. We present a forward model to simulate galaxy surveys, where we extend the Ultra-Fast Image Generator to include galaxy clustering. We use the distribution functions of the galaxy properties, derived from a forward model adjusted to observations. This population model jointly describes the luminosity functions, sizes, ellipticities, SEDs and apparent magnitudes. To simulate the positions of galaxies, we then use a two-parameter relation between galaxies and halos with Subhalo Abundance Matching (SHAM). We simulate the halos and subhalos using the fast PINOCCHIO code, and a method to extract the surviving subhalos from the merger history. Our simulations contain a red and a blue galaxy population, for which we build a SHAM model based on star formation quenching. For central galaxies, mass quenching is controlled with the parameter Mlimit, with blue galaxies residing in smaller halos. For satellite galaxies, environmental quenching is implemented with the parameter tquench, where blue galaxies occupy only recently merged subhalos. We build and test our model by comparing to imaging data from the Dark Energy Survey Year 1. To ensure completeness in our simulations, we consider the brightest galaxies with i < 20. We find statistical agreement between our simulations and the data for two-point correlation functions on medium to large scales. Our model provides constraints on the two SHAM parameters Mlimit and tquench and offers great prospects for the quick generation of galaxy mock catalogues, optimized to agree with observations.

CSST large-scale structure analysis pipeline: I. Constructing reference mock galaxy redshift surveys

ABSTRACT In this paper, we set out to construct a set of reference mock galaxy redshift surveys (MGRSs) for the future Chinese Space-station Survey Telescope (CSST) observation, where subsequent survey selection effects can be added and evaluated. This set of MGRSs is generated using the dark matter subhaloes extracted from a high-resolution Jiutian N-body simulation of the standard lambda-cold dark matter cosmogony with Ωm = 0.3111, ΩΛ = 0.6889, and σ8 = 0.8102. The simulation has a box size of 1 h−1Gpc, and consists of 61443 particles with mass resolution $3.723 \times 10^{8} \:h^{-1}\rm M_\odot$. In order to take into account the effect of redshift evolution, we first use all 128 snapshots in the Jiutian simulation to generate a light-cone halo/subhalo catalogue. Next, galaxy luminosities are assigned to the main and subhalo populations using the subhalo abundance matching (SHAM) method with the DESI (Dark Energy Spectroscopic Instrument) z-band luminosity functions at different redshifts. Multiband photometries, as well as images, are then assigned to each mock galaxy using a 3D parameter space nearest-neighbour sampling of the DESI LS (Legacy Imaging Survey) observational galaxies and groups. Finally, the CSST and DESI LS survey geometry and magnitude limit cuts are applied to generate the required MGRSs. As we have checked, this set of MGRSs can generally reproduce the observed galaxy luminosity/mass functions within 0.1 dex for galaxies with L &amp;gt; 108 h-2 L⊙ (or M* &amp;gt; 108.5 h-2M⊙) and within 1σ level for galaxies with L &amp;lt; 108 h-2 L⊙ (or M* &amp;lt; 108.5 h-2 M⊙). Together with the CSST slitless spectra and redshifts for our DESI LS seed galaxies that are under construction, we will set out to test various slitless observational selection effects in subsequent probes.

Fast Generation of Mock Galaxy Catalogs with COLA

We investigate the feasibility of using the comoving Lagrangian acceleration (COLA) technique to efficiently generate galaxy mock catalogs that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance-matching (SHAM) procedure with COLA simulations, using only three free parameters: the scatter magnitude (σ scat) in SHAM, the initial redshift (z init) of the COLA simulation, and the time stride (da) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range z ∈ [0.45, 0.55]. We perform GADGET simulation and low-resolution COLA simulations with various combinations of (z init, da), each using 10243 particles in an 800 h −1 Mpc box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal σ scat. We have found that by setting z init = 29 and da = 1/30, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4–20 h −1 Mpc, with a computational cost lower by 2 orders of magnitude than that of the GADGET N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows a similar performance of the GADGET mock and COLA mock catalogs with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogs for different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogs for emulators and machine-learning analyses to explore the large-scale structure of the Universe.

The Uchuu–SDSS galaxy light-cones: a clustering, redshift space distortion and baryonic acoustic oscillation study

ABSTRACT We present the data release of the Uchuu–SDSS galaxies: a set of 32 high-fidelity galaxy light-cones constructed from the large Uchuu 2.1 trillion particles N-body simulation using Planck cosmology. We adopt subhalo abundance matching to populate the Uchuu-box halo catalogues with SDSS galaxy luminosities. These box catalogues generated at several redshifts are combined to create a set of light-cones with redshift-evolving galaxy properties. The Uchuu–SDSS galaxy light-cones are built to reproduce the footprint and statistical properties of the SDSS main galaxy survey, along with stellar masses and star formation rates. This facilitates a direct comparison of the observed SDSS and simulated Uchuu–SDSS data. Our light-cones reproduce a large number of observational results, such as the distribution of galaxy properties, galaxy clustering, stellar mass functions, and halo occupation distributions. Using simulated and real data, we select samples of bright red galaxies at zeff = 0.15 to explore redshift space distortions and baryon acoustic oscillations (BAO) by fitting the full two-point correlation function and the BAO peak. We create a set of 5100 galaxy light-cones using GLAM N-body simulations to compute covariance errors. We report a $\sim 30~{{\ \rm per\ cent}}$ precision increase on fσ8 and the pre-reconstruction BAO scale, due to our better estimate of the covariance matrix. From our BAO-inferred α∥ and α⊥ parameters, we obtain the first SDSS measurements of the Hubble and angular diameter distances $D_\mathrm{H}(z=0.15) / r_d = 27.9^{+3.1}_{-2.7}$, $D_\mathrm{M}(z=0.15) / r_d = 5.1^{+0.4}_{-0.4}$. Overall, we conclude that the Planck Λ CDM cosmology nicely explains the observed large-scale structure statistics of SDSS. All data sets are made publicly available.

The DESI One-Percent Survey: A Concise Model for the Galactic Conformity of Emission-line Galaxies

Galactic conformity is the phenomenon whereby a galaxy of a certain physical property is correlated with its neighbors of the same property, implying a possible causal relationship. The observed auto correlations of emission-line galaxies (ELGs) from the highly complete DESI One-Percent Survey exhibit a strong clustering signal on small scales, providing clear evidence for the conformity effect of ELGs. Building upon the original subhalo abundance-matching (SHAM) method developed by Gao et al., we propose a concise conformity model to improve the ELG–halo connection. In this model, the number of satellite ELGs is boosted by a factor of ∼5 in the halos whose central galaxies are ELGs. We show that the mean ELG satellite number in such central halos is still smaller than 1 and that the model does not significantly increase the overall satellite fraction. With this model, we can well recover the ELG auto correlations to the smallest scales explored with the current data (i.e., r p > 0.03 Mpc h −1 in real space and at s > 0.3 Mpc h −1 in redshift space), while the cross correlations between luminous red galaxies and ELGs are nearly unchanged. Although our SHAM model has only eight parameters, we further verify that it can accurately describe the ELG clustering in the entire redshift range from z = 0.8 to 1.6. We therefore expect that this method can be used to generate high-quality ELG lightcone mocks for DESI.

The effective field theory of large-scale structure and multi-tracer II: redshift space and realistic tracers

We extend the multi-tracer (MT) formalism of the effective field theory of large-scale structure to redshift space, comparing the results of MT to a single-tracer analysis when extracting cosmological parameters from simulations. We used a sub-halo abundance matching method to obtain more realistic multi-tracer galaxy catalogs constructed from N-body simulations. Considering different values for the sample shot noise and volume, we show that the MT error bars on A s, ω cdm, and h in a full-shape analysis are approximately 50% smaller relative to ST. We find that cosmological and bias coefficients from MT are less degenerate, indicating that the MT parameter basis is more orthogonal. We conclude that using MT combined with perturbation theory is a robust and competitive way to accommodate the information present in the mildly non-linear scales.

The DESI One-Percent Survey: exploring a generalized SHAM for multiple tracers with the UNIT simulation

ABSTRACT We perform SubHalo Abundance Matching (SHAM) studies on UNIT simulations with {σ, Vceil, vsmear}-SHAM and {σ, Vceil, fsat}-SHAM. They are designed to reproduce the clustering on 5–30 $\, {\, h^{-1}\, {\rm Mpc}}$ of luminous red galaxies (LRGs), emission-line galaxies (ELGs), and quasi-stellar objects (QSOs) at 0.4 &amp;lt; z &amp;lt; 3.5 from DESI (Dark Energy Spectroscopic Instrument) One Percent Survey. Vceil is the incompleteness of the massive host (sub)haloes and is the key to the generalized SHAM. vsmear models the clustering effect of redshift uncertainties, providing measurements consistent with those from repeat observations. A free satellite fraction fsat is necessary to reproduce the clustering of ELGs. We find ELGs present a more complex galaxy–halo mass relation than LRGs reflected in their weak constraints on σ. LRGs, QSOs, and ELGs show increasing Vceil values, corresponding to the massive galaxy incompleteness of LRGs, the quenched star formation of ELGs and the quenched black hole accretion of QSOs. For LRGs, a Gaussian vsmear presents a better profile for subsamples at redshift bins than a Lorentzian profile used for other tracers. The impact of the statistical redshift uncertainty on ELG clustering is negligible. The best-fitting satellite fraction for DESI ELGs is around 4 per cent, lower than previous estimations for ELGs. The mean halo mass log10(〈Mvir〉) in ${{\, h^{-1}\, \mbox{M}_\odot }}{}$ for LRGs, ELGs, and QSOs are 13.16 ± 0.01, 11.90 ± 0.06, and 12.66 ± 0.45, respectively. Our generalized SHAM algorithms facilitate the production of multitracer galaxy mocks for cosmological tests.

The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching

We use subhalo abundance and age distribution matching to create magnitude-limited mock galaxy catalogs at z ∼ 0.43, 0.52, and 0.63 with z-band and 3.4 μm W1-band absolute magnitudes and r − z and r − W1 colors. From these magnitude-limited mocks, we select mock luminous red galaxy (LRG) samples according to the (r − z)-based (optical) and (r − W1)-based (infrared) selection criteria for the LRG sample of the Dark Energy Spectroscopic Instrument (DESI) survey. Our models reproduce the number densities, luminosity functions, color distributions, and projected clustering of the DESI Legacy Surveys that are the basis for DESI LRG target selection. We predict the halo occupation statistics of both optical and IR DESI LRGs at fixed cosmology and assess the differences between the two LRG samples. We find that IR-based SHAM modeling represents the differences between the optical and IR LRG populations better than using the z band and that age distribution matching overpredicts the clustering of LRGs, implying that galaxy color is uncorrelated with halo age in the LRG regime. Both the optical and IR DESI LRG target selections exclude some of the most luminous galaxies that would appear to be LRGs based on their position on the red sequence in optical color–magnitude space. Both selections also yield populations with a nontrivial LRG–halo connection that does not reach unity for the most massive halos. We find that the IR selection achieves greater completeness (≳90%) than the optical selection across all redshift bins studied.

On the origin of the evolution of the halo occupation distribution

ABSTRACT We use the TNG300 magnetohydrodynamic simulation and mock catalogues built using subhalo abundance matching (SHAM) to study the origin of the redshift evolution of the halo occupation distribution (HOD). We analyse stellar-mass selected galaxy samples with fixed number densities, spanning the redshift range 0 ≤ z ≤ 3. We measure their halo occupation functions and fit the HOD parameters to study their evolution over cosmic time. The TNG300 galaxy population strongly depends on the baryonic physics implemented in the simulation. In contrast, the galaxy population predicted by a basic SHAM model without scatter is a direct result of the cosmology of the dark matter simulation. We find that the HOD evolution is similar for both models and is consistent with a previous study of the HOD evolution in semi-analytical models. Specifically, this is the case for the ratio between the characteristic halo masses for hosting central and satellite galaxies. The only HOD parameter whose evolution varies across models is σlogM, which contains information about the stellar mass–halo mass relation of the galaxies but does not strongly impact galaxy clustering. We also demonstrate that the dependence on the specific values of the cosmological parameters is small. We conclude that the cosmology of the galaxy sample, i.e. the cosmological hierarchical growth of structure, and not the baryonic physics prescriptions, governs the evolution of the HOD for stellar mass-selected samples. These results have important implications for populating simulated light-cones with galaxies and can facilitate the interpretation of clustering data at different redshifts.

Consistent clustering and lensing of SDSS-III BOSS galaxies with an extended abundance matching formalism

ABSTRACT Several analyses have shown that Λ cold dark matter-based models cannot jointly describe the clustering (GC) and galaxy–galaxy lensing (GGL) of galaxies in the Sloan Digital Sky Survey-III (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS), which is commonly known as the ‘lensing-is-low problem’. In this work, we show that an extension of Subhalo Abundance Matching, dubbed SHAMe, successfully solves this problem. First, we show that this model accurately reproduces the GC and GGL of a mock galaxy sample in the TNG300 hydrodynamic simulation with properties analogous to those of BOSS galaxies. Then, we switch our attention to observed BOSS galaxies at z = 0.31−0.43, and we attempt to reproduce their GC and GGL by evaluating SHAMe on two different simulations: one adopting best-fitting cosmological parameters from Planck and the other from weak gravitational lensing surveys (Low S8), where the amplitude of matter fluctuations is lower for the latter. We find excellent agreement between SHAMe predictions and observations for both cosmologies, indicating that the lensing-is-low problem originates from approximations in previous theoretical descriptions of the data. The main difference between SHAMe results in these cosmologies is the level of galaxy assembly bias, which is approximately 20 per cent and 10 per cent for Planck and Low S8, respectively. These results highlight the dangers of employing oversimplified models to analyse current large-scale structure data sets, and the need for realistic yet flexible descriptions of the galaxy–halo connection.

Correction to: Subhalo abundance matching using progenitor mass at varying redshift: two modes of stellar mass growth imprinted into the Subaru HSC galaxy clustering

Correction to: Subhalo abundance matching using progenitor mass at varying redshift: two modes of stellar mass growth imprinted into the Subaru HSC galaxy clustering

The MillenniumTNG Project: inferring cosmology from galaxy clustering with accelerated N-body scaling and subhalo abundance matching

ABSTRACT We introduce a novel technique for constraining cosmological parameters and galaxy assembly bias using non-linear redshift-space clustering of galaxies. We scale cosmological N-body simulations and insert galaxies with the SubHalo Abundance Matching extended (SHAMe) empirical model to generate over 175 000 clustering measurements spanning all relevant cosmological and SHAMe parameter values. We then build an emulator capable of reproducing the projected galaxy correlation function at the monopole, quadrupole, and hexadecapole level for separations between $0.1\, h^{-1}\, {\rm Mpc}$ and $25\, h^{-1}\, {\rm Mpc}$. We test this approach by using the emulator and Monte Carlo Markov Chain (MCMC) inference to jointly estimate cosmology and assembly bias parameters both for the MTNG740 hydrodynamic simulation and for a semi-analytical model (SAM) galaxy formation built on the MTNG740-DM dark matter-only simulation, obtaining unbiased results for all cosmological parameters. For instance, for MTNG740 and a galaxy number density of $n\sim 0.01 h^{3}\, {\rm Mpc}^{-3}$, we obtain $\sigma _{8}=0.799^{+0.039}_{-0.044}$ and $\Omega _\mathrm{M}h^2= 0.138^{+ 0.025}_{- 0.018}$ (which are within 0.4 and 0.2σ of the MTNG cosmology). For fixed Hubble parameter (h), the constraint becomes $\Omega _\mathrm{M}h^2= 0.137^{+ 0.011}_{- 0.012}$. Our method performs similarly well for the SAM and for other tested sample densities. We almost always recover the true amount of galaxy assembly bias within 1σ. The best constraints are obtained when scales smaller than $2\, h^{-1}\, {\rm Mpc}$ are included, as well as when at least the projected correlation function and the monopole are incorporated. These methods offer a powerful way to constrain cosmological parameters using galaxy surveys.

Subhalo abundance matching using progenitor mass at varying redshift: two modes of stellar mass growth imprinted into the Subaru HSC galaxy clustering

ABSTRACT We propose a novel subhalo abundance matching (SHAM) model that uses the virial mass of the main progenitor of each (sub)halo Mprog as a proxy of the galaxy stellar mass M* at the time of observation. This Mprog model predicts the two-point correlation functions depending on the choice of the epoch zprog at which Mprog is quoted. With zprog as a fitting parameter, we apply the Mprog model to the angular correlation functions measured with varying stellar mass thresholds from M*, lim/(h−2 M⊙) = 1011–108.6 using a sample of galaxies at z ≃ 0.4 from the Subaru Hyper Suprime-Cam survey. The Mprog model can reproduce the observations very well over 10 h−1kpc–10 h−1Mpc. We find that, for the samples of 109.2 ≤ M*, lim/(h−2 M⊙) ≤ 1010.2, the correlation functions predicted by the widely used Vpeak model lack amplitudes at ≲1 h−1 Mpc, suggesting that Mprog is a better proxy of the galaxy stellar mass than conventional Vpeak. The zprog parameter is highest (zprog ≃ 3) for intermediate mass galaxies at M* ≃ 109.9 h−2 M⊙, and becomes smaller down to zprog ≃ 1 for both lower and higher mass galaxies. We interpret these trends as reflecting the downsizing in the in situ star formation in lower mass galaxies and the larger contribution of the ex situ stellar mass growth in higher mass galaxies.

Inferring dark matter halo properties for H i-selected galaxies

ABSTRACT We set constraints on the dark matter halo mass and concentration of ∼22 000 individual galaxies visible both in $\rm{H}\, {\small I}$ (from the ALFALFA survey) and optical light (from the Sloan Digital Sky Survey). This is achieved by combining two Bayesian models, one for the $\rm{H}\, {\small I}$ line width as a function of the stellar and neutral hydrogen mass distributions in a galaxy using kinematic modelling, and the other for the galaxy’s total baryonic mass using the technique of inverse subhalo abundance matching. We hence quantify the constraining power on halo properties of spectroscopic and photometric observations, and assess their consistency. We find good agreement between the two sets of posteriors, although there is a sizeable population of low-line width galaxies that favour significantly smaller dynamical masses than expected from abundance matching (especially for cuspy halo profiles). Abundance matching provides significantly more stringent bounds on halo properties than the $\rm{H}\, {\small I}$ line width, even with a mass–concentration prior included, although combining the two provides a mean gain of 40 per cent for the sample when fitting an NFW profile. We also use our kinematic posteriors to construct a baryonic mass–halo mass relation, which we find to be near power law, and with a somewhat shallower slope than expected from abundance matching. Our method demonstrates the potential of combining photometric and spectroscopic observations to precisely map out the dark matter distribution at the galaxy scale using upcoming $\rm{H}\, {\small I}$ surveys such as the SKA.

A New Stellar Mass Proxy for Subhalo Abundance Matching

Subhalo abundance matching (SHAM) has played an important role in improving our understanding of how galaxies populate their host dark matter halos. In essence, the SHAM framework is to find a dark matter halo property that best correlates with an attribute of galaxies, such as stellar mass. The peak value of the maximum circular velocity () a halo/subhalo has ever attained throughout its lifetime, V peak, has been a popular choice for SHAM. A recent study by Tonnesen & Ostriker suggested that quantity ϕ, which combines the present-day and the peak value of halo dark matter mass, performs better in predicting stellar mass than V peak. Inspired by their approach, in this work, we find that further improvement can be achieved by a quantity ψ 5 that combines the 90th percentile of a halo/subhalo has ever achieved with the 60th percentile of the dark matter halo time variation rate. Tests based on the simulation TNG300 of the IllustrisTNG project show that our new SHAM scheme, with just three free parameters, can improve the stellar mass prediction and mass-dependent clustering by 15% and 18% from ϕ, respectively, over the redshift range z = 0–2.

Consistent and simultaneous modelling of galaxy clustering and galaxy–galaxy lensing with subhalo abundance matching

ABSTRACT The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Sub-Halo Abundance Matching extended model (SHAMe), dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift–space galaxy correlation function and galaxy–galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r ∈ [0.6 − 30]h−1 Mpc. Modelling the baryonic processes in galaxy–galaxy lensing with a baryonification scheme allows SHAMe’s range of validity to be extended to r ∈ [0.1 − 30] h−1 Mpc. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy–galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys.

A sparse regression approach for populating dark matter haloes and subhaloes with galaxies

ABSTRACT We use sparse regression methods (SRMs) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter haloes. SRMs are machine learning algorithms that provide a framework for modelling the governing equations of a system from data. In contrast with other machine learning algorithms, the solutions of SRM methods are simple and depend on a relatively small set of adjustable parameters. We collect data from 35 459 galaxies from the EAGLE simulation using 19 redshift slices between z = 0 and z = 4 to parametrize the mass evolution of the host haloes. Using an appropriate formulation of input parameters, our methodology can model satellite and central haloes using a single predictive model that achieves the same accuracy as when predicted separately. This allows us to remove the somewhat arbitrary distinction between those two galaxy types and model them based only on their halo growth history. Our models can accurately reproduce the total galaxy stellar mass function and the stellar mass-dependent galaxy correlation functions (ξ(r)) of EAGLE. We show that our SRM model predictions of ξ(r) is competitive with those from subhalo abundance matching and might be comparable to results from extremely randomized trees. We suggest SRM as an encouraging approach for populating the haloes of dark matter only simulations with galaxies and for generating mock catalogues that can be used to explore galaxy evolution or analyse forthcoming large-scale structure surveys.

Modeling Redshift-space Clustering with Abundance Matching

We explore the degrees of freedom required to jointly fit projected and redshift-space clustering of galaxies selected in three bins of stellar mass from the Sloan Digital Sky Survey Main Galaxy Sample (SDSS MGS) using a subhalo abundance matching (SHAM) model. We employ emulators for relevant clustering statistics in order to facilitate our analysis, leading to large speed gains with minimal loss of accuracy. We are able to simultaneously fit the projected and redshift-space clustering of the two most massive galaxy samples that we consider with just two free parameters: scatter in stellar mass at fixed SHAM proxy, and the dependence of the SHAM proxy on dark matter halo concentration. We find some evidence for models that include velocity bias, but including orphan galaxies improves our fits to the lower-mass samples significantly. We also model the clustering signals of specific star formation rate (sSFR) selected samples using conditional abundance matching (CAM). We obtain acceptable fits to projected and redshift-space clustering as a function of sSFR and stellar mass using two CAM variants, although the fits are worse than for stellar-mass-selected samples alone. By incorporating nonunity correlations between the CAM proxy and sSFR, we are able to resolve previously identified discrepancies between CAM predictions and SDSS observations of the environmental dependence of quenching for isolated central galaxies.