How are galaxies assigned to halos? Searching for assembly bias in the SDSS galaxy clustering

Clustering of dark matter halos has been shown to depend on halo properties beyond mass such as halo concentration, a phenomenon referred to as halo assembly bias. Standard halo occupation models (HOD) in large scale structure studies assume that halo mass alone is sufficient in characterizing the connection between galaxies and halos. Modeling of galaxy clustering can face systematic effects if the number of galaxies within a halo is correlated with other halo properties. Using the Small MultiDark-Planck high resolution $N$-body simulation and the clustering measurements of the Sloan Digital Sky Survey (SDSS) DR7 main galaxy sample, we investigate the extent to which the concentration-dependence of halo occupation can be constrained. Furthermore, we study how allowing for the concentration dependence can improve our modeling of galaxy clustering. Our constraints on HOD with assembly bias suggest that satellite population is not correlated with halo concentration at fixed halo mass. At fixed halo mass, our constraints favor lack of correlation between the occupation of centrals and halo concentration in the most luminous samples ($M_{\rm r}<-21.5,-21$), and modest correlation in the $M_{\rm r}<-20.5,-20, -19.5$ samples. We show that in comparison with abundance-matching mock catalogs, our findings suggest qualitatively similar but modest levels of the impact of halo assembly bias on galaxy clustering. The effect is only present in the central occupation and becomes less significant in brighter galaxy samples. Furthermore, by performing model comparison based on information criteria, we find that in most cases, the standard mass-only HOD model is still favored by the observations.Comment: Accepted for publication in Ap

Star Formation Quenching Timescale of Central Galaxies in a Hierarchical Universe

Central galaxies make up the majority of the galaxy population, including the majority of the quiescent population at $\mathcal{M}_* > 10^{10}\mathrm{M}_\odot$. Thus, the mechanism(s) responsible for quenching central galaxies plays a crucial role in galaxy evolution as whole. We combine a high resolution cosmological $N$-body simulation with observed evolutionary trends of the "star formation main sequence," quiescent fraction, and stellar mass function at $z < 1$ to construct a model that statistically tracks the star formation histories and quenching of central galaxies. Comparing this model to the distribution of central galaxy star formation rates in a group catalog of the SDSS Data Release 7, we constrain the timescales over which physical processes cease star formation in central galaxies. Over the stellar mass range $10^{9.5}$ to $10^{11} \mathrm{M}_\odot$ we infer quenching e-folding times that span $1.5$ to $0.5\; \mathrm{Gyr}$ with more massive central galaxies quenching faster. For $\mathcal{M}_* = 10^{10.5}\mathrm{M}_\odot$, this implies a total migration time of $\sim 4~\mathrm{Gyrs}$ from the star formation main sequence to quiescence. Compared to satellites, central galaxies take $\sim 2~\mathrm{Gyrs}$ longer to quench their star formation, suggesting that different mechanisms are responsible for quenching centrals versus satellites. Finally, the central galaxy quenching timescale we infer provides key constraints for proposed star formation quenching mechanisms. Our timescale is generally consistent with gas depletion timescales predicted by quenching through strangulation. However, the exact physical mechanism(s) responsible for this still remain unclear.Comment: 16 pages, 11 figure

Likelihood Non-Gaussianity in Large-Scale Structure Analyses

Standard present day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference --- that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. (2017) power spectrum multipole ($P_\ell$) analysis and the Sinha et al. (2017) group multiplicity function ($\zeta$) analysis. Using non-parametric divergence estimators on mock catalogs originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the $P_\ell$ and $\zeta$ likelihoods. We then use Gaussian mixture density estimation and Independent Component Analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian $pseudo$-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. (2017) and Sinha et al. (2017) parameters. Likelihood non-Gaussianity shifts the $f\sigma_8$ constraint by $-0.44\sigma$, but otherwise, does not significantly impact the overall parameter constraints of Beutler et al. (2017). For the $\zeta$ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of Sinha et al. (2017) halo occupation parameters. For $\log M_1$ and $\alpha$, the posteriors are shifted by $+0.43\sigma$ and $-0.51\sigma$ and broadened by $42\%$ and $66\%$, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints.Comment: 33 pages, 7 figure

Neural Stellar Population Synthesis Emulator for the DESI PROVABGS

The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of $>10$ million DESI Bright Galaxy Survey (BGS) galaxies. Each posterior distribution will be inferred from joint Bayesian modeling of observed photometry and spectroscopy using Markov Chain Monte Carlo sampling and the [arXiv:2202.01809] stellar population synthesis (SPS) model. To make this computationally feasible, PROVABGS will use a neural emulator for the SPS model to accelerate the posterior inference. In this work, we present how we construct the emulator using the [arXiv:1911.11778] approach and verify that it can be used to accurately infer galaxy properties. We confirm that the emulator is in excellent agreement with the original SPS model with $\ll 1\%$ error and is $100\times$ faster. In addition, we demonstrate that the posteriors of galaxy properties derived using the emulator are also in excellent agreement with those inferred using the original model. The neural emulator presented in this work is essential in bypassing the computational challenge posed in constructing the PROVABGS catalog. Furthermore, it demonstrates the advantages of emulation for scaling sophisticated analyses to millions of galaxies.Comment: 9 pages, 5 figures, submitted to ApJ

Cosmology with Galaxy Photometry Alone

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. (2022; arXiv:2201.02202) recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g. optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of $\sim$20,000 NASA-Sloan Atlas galaxies, we constrain $\Omega_m = 0.323^{+0.075}_{-0.095}$ and $\sigma_8 = 0.799^{+0.088}_{-0.085}$.Comment: 15 pages, 7 figures, submitted to ApJL, comments welcom

Cosmological Information in Skew Spectra of Biased Tracers in Redshift Space

Extracting the non-Gaussian information encoded in the higher-order clustering statistics of the large-scale structure is key to fully realizing the potential of upcoming galaxy surveys. We investigate the information content of the redshift-space {\it weighted skew spectra} of biased tracers as efficient estimators for 3-point clustering statistics. The skew spectra are constructed by correlating the observed galaxy field with an appropriately-weighted square of it. We perform numerical Fisher forecasts using two synthetic datasets; the halo catalogs from the Quijote N-body simulations and the galaxy catalogs from the Molino suite. The latter serves to understand the effect of marginalization over a more complex matter-tracer biasing relation. Compared to the power spectrum multipoles, we show that the skew spectra substantially improve the constraints on six parameters of the $\nu\Lambda$CDM model, $\{\Omega_m, \Omega_b, h, n_s, \sigma_8, M_\nu\}$. Imposing a small-scale cutoff of $k_{\rm max}=0.25 \, {\rm Mpc}^{-1}h$, the improvements from skew spectra alone range from 23% to 62% for the Quijote halos and from 32% to 71% for the Molino galaxies. Compared to the previous analysis of the bispectrum monopole on the same data and using the same range of scales, the skew spectra of Quijote halos provide competitive constraints. Conversely, the skew spectra outperform the bispectrum monopole for all cosmological parameters for the Molino catalogs. This may result from additional anisotropic information, particularly enhanced in the Molino sample, that is captured by the skew spectra but not by the bispectrum monopole. Our stability analysis of the numerical derivatives shows comparable convergence rates for the power spectrum and the skew spectra, indicating potential underestimation of parameter uncertainties by at most 30%.Comment: 43 pages, 25 figure

Differentiable Stochastic Halo Occupation Distribution

In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of Halo Occupancy Distribution (HOD) models which are used to connect galaxies with their dark matter halos. Using a combination of continuous relaxation and gradient parameterization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalog generated from the Bolshoi simulation using the Zheng et al. 2007 HOD model and find near identical posteriors as standard Markov Chain Monte Carlo techniques with an increase of ~8x in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.Comment: 10 pages, 6 figures, comments welcom

Halo histories versus galaxy properties at z = 0 – III. The properties of star-forming galaxies

We measure how the properties of star-forming central galaxies correlate with large-scale environment, δ, measured on 10 h^(−1) Mpc scales. We use galaxy group catalogues to isolate a robust sample of central galaxies with high purity and completeness. The galaxy properties we investigate are star formation rate (SFR), exponential disc scale length R_(exp), and Sersic index of the galaxy light profile, n_S. We find that, at all stellar masses, there is an inverse correlation between SFR and δ, meaning that above-average star-forming centrals live in underdense regions. For n_S and R_(exp), there is no correlation with δ at M* ≲ 10^(10.5)M⊙, but at higher masses there are positive correlations; a weak correlation with R_(exp) and a strong correlation with n_S. These data are evidence of assembly bias within the star-forming population. The results for SFR are consistent with a model in which SFR correlates with present-day halo accretion rate, M_h. In this model, galaxies are assigned to haloes using the abundance-matching ansatz, which maps galaxy stellar mass onto halo mass. At fixed halo mass, SFR is then assigned to galaxies using the same approach, but M_h is used to map onto SFR. The best-fitting model requires some scatter in the M_h –SFR relation. The R_(exp) and n_S measurements are consistent with a model in which both of these quantities are correlated with the spin parameter of the halo, λ. Halo spin does not correlate with δ at low halo masses, but for higher mass haloes, high-spin haloes live in higher density environments at fixed M_h. Put together with the earlier instalments of this series, these data demonstrate that quenching processes have limited correlation with halo formation history, but the growth of active galaxies, as well as other detailed galaxies properties, are influenced by the details of halo assembly