Galaxy Clustering Measurements Research Articles

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.

Black Hole–Halo Mass Relation from UNIONS Weak Lensing

This Letter presents, for the first time, direct constraints on the black hole–halo mass relation using weak gravitational-lensing measurements. We construct type I and type II active galactic nucleus (AGN) samples from the Sloan Digital Sky Survey, with a mean redshift of 0.4 (0.1) for type I (type II) AGNs. This sample is cross correlated with weak-lensing shear from the Ultraviolet Near Infrared Optical Northern Survey. We compute the excess surface mass density of the halos associated with 36,181 AGNs from 94,308,561 lensed galaxies and fit the halo mass in bins of black hole mass. We find that more massive AGNs reside in more massive halos. The relation between halo mass and black hole mass is well described by a power law of slope 0.6 for both type I and type II samples, in agreement with models that link black hole growth to baryon feedback. We see no dependence on AGN type or redshift in the black hole–halo mass relation below a black hole mass of 108.5 M ⊙. Above that mass, we find more massive halos for the low-z type II sample compared to the high-z type I sample, but this difference may be interpreted as systematic error in the black hole mass measurements. Our results are consistent with previous measurements for non-AGN galaxies. At a fixed black hole mass, our weak-lensing halo masses are consistent with galaxy rotation curves but significantly lower than galaxy-clustering measurements. Finally, our results are broadly consistent with state-of-the-art hydrodynamical cosmological simulations, providing a new constraint for black hole masses in simulations.

Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements

ABSTRACT Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy two-point statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (two-point function) by joint inference of contaminants in the galaxy overdensity field (one-point function) using a maximum-likelihood estimator (MLE). We test this methodology with Kilo-Degree Survey-like mock galaxy catalogues and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the sub-per cent level and reduces numerous additive and multiplicative systematics from $1.5 \sigma$ to less than $0.1\sigma$ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other two-point correlation measurements.

Faster cosmological analysis with power spectrum without simulations

ABSTRACT Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the cosmic microwave background. However, the inclusion of multiple overlapping tracers, redshift bins, and more non-linear scales means that generating the necessary ensemble of simulations for model-fitting presents a computational burden. In this work, we combine full-shape fitting of galaxy power spectra, analytical covariance matrix estimates, the massively optimized parameter estimation and data compression (MOPED) method, and the Taylor expansion interpolation of the power spectrum for the first time to constrain the cosmological parameters directly from a state-of-the-art set of galaxy clustering measurements. We find it takes less than a day to compute the analytical covariance while it takes several months to calculate the simulated ones. Combining MOPED with the Taylor expansion interpolation of the power spectrum, we can constrain the cosmological parameters in just a few hours instead of a few days. We also find that even without a priori knowledge of the best-fitting cosmological or galaxy bias parameters, the analytical covariance matrix with the MOPED compression still gives consistent cosmological constraints to within 0.1σ after two iterations. Therefore, the pipeline we have developed here can significantly speed up the analysis for future surveys.

Mass reconstruction and noise reduction with cosmic-web environments

ABSTRACT The clustering of galaxies and their connections to their initial conditions is a major means by which we learn about cosmology. However, the stochasticity between galaxies and their underlying matter field is a major limitation for precise measurements of galaxy clustering. Efforts have been made with an optimal weighting scheme to reduce this stochasticity using the mass-dependent clustering of dark matter haloes. Here, we show that this is not optimal. We demonstrate that the cosmic-web environments (voids, sheets, filaments, and knots) of haloes, when combined linearly with the linear bias, provide extra information for reducing stochasticity in terms of two-point statistics. Using the environmental information alone can increase the signal-to-noise of clustering by a factor of 3 better than the white-noise level at the scales of the baryon acoustic oscillations. The information about the environment and halo mass are complementary. Their combination increases the signal-to-noise by another factor of 2-3. The information about the cosmic web correlates with other properties of haloes, including halo concentrations and tidal forces – all are related to the assembly bias of haloes.

The Kullback–Leibler Divergence and the Convergence Rate of Fast Covariance Matrix Estimators in Galaxy Clustering Analysis

We present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two probability distributions. As a case study, we analyze the delete-d jackknife estimator for the covariance matrix of the galaxy correlation function. We introduce the information factor or the normalized KL divergence with the help of a set of baseline covariance matrices to diagnose the information contained in the jackknife covariance matrix. Using a set of quick particle mesh mock catalogs designed for the Baryon Oscillation Spectroscopic Survey DR11 CMASS galaxy survey, we find that the jackknife resampling method succeeds in recovering the covariance matrix with 10 times fewer simulation mocks than that of the baseline method at small scales (s ≤ 40 h −1 Mpc). However, the ability to reduce the number of mock catalogs is degraded at larger scales due to the increasing bias on the jackknife covariance matrix. Note that the analysis in this paper can be applied to any fast estimator of the covariance matrix for galaxy clustering measurements.

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of k max = 0.4 Mpc-1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.

Galaxy clustering measurements out to redshift z ˜ 8 from Hubble Legacy Fields

ABSTRACT We present a novel approach for measuring the two-point correlation function of galaxies in narrow pencil beam surveys with varying depths. Our methodology is utilized to expand high-redshift galaxy clustering investigations up to z ∼ 8 by analysing a comprehensive sample consisting of Ng = 160 Lyman break galaxy candidates obtained through optical and near-infrared photometric data within the CANDELS GOODS data sets from the Hubble Space Telescope Legacy Fields. For bright sources with MUV < −19.8, we determine a galaxy bias of b = 9.33 ± 4.90 at $\overline{z} = 7.7$ and a correlation length of r0 = 10.74 ± 7.06 $h^{-1}\, \mathrm{Mpc}$. We obtain similar results for the XDF, with a galaxy bias measurement of b = 8.26 ± 3.41 at the same redshift for a slightly fainter sample with a median luminosity of MUV = −18.4. By comparing with dark-matter halo bias and employing abundance matching, we deduce a characteristic halo mass of Mh ∼ 1011.5 M⊙ and a duty cycle close to unity. To validate our approach for variable-depth data sets, we replicate the analysis in a region with near-uniform depth using a standard two-point correlation function estimator, yielding consistent outcomes. Our study not only provides a valuable tool for future utilization in JWST data sets but also suggests that the clustering of early galaxies continues to increase with redshift beyond z ≳ 8, potentially contributing to the existence of protocluster structures observed in early JWST imaging and spectroscopic surveys at z ≳ 8.

The Galaxy Number Density Profile of Halos

More precise measurements of galaxy clustering will be provided by the next generation of galaxy surveys, such as DESI, WALLABY, and the Square Kilometre Array. To utilize this information to improve our understanding of the Universe, we need to accurately model the distribution of galaxies in their host dark matter halos. In this work, we present a new galaxy number density profile of halos, which makes predictions for the positions of galaxies in the host halo, different to the widely adopted Navarro–Frenk–White (NFW) profile, since galaxies tend to be found more in the outskirts of halos (nearer the virial radius) than an NFW profile. The parameterized galaxy number density profile model of halos is fit and tested using the Dark Sage semi-analytic model of galaxy formation. We find that our galaxy number density profile model of halos can accurately reproduce the halo occupation distribution and galaxy two-point correlation function of the Dark Sage simulation. We also derive the analytic expressions for the circular velocity and gravitational potential energy for this profile model. We use the SDSS Data Release 10 galaxy group catalog to validate this galaxy number density profile model of halos. Compared to the NFW profile, we find that our model more accurately predicts the positions of galaxies in their host halo and the galaxy two-point correlation function.

Evidence for Suppression of Structure Growth in the Concordance Cosmological Model.

We present evidence for a suppressed growth rate of large-scale structure during the dark-energy-dominated era. Modeling the growth rate of perturbations with the "growth index" γ, we find that current cosmological data strongly prefer a higher growth index than the value γ=0.55 predicted by general relativity in a flat Lambda cold dark matter cosmology. Both the cosmic microwave background data from Planck and the large-scale structure data from weak lensing, galaxy clustering, and cosmic velocities separately favor growth suppression. When combined, they yield γ=0.633_{-0.024}^{+0.025}, excluding γ=0.55 at a statistical significance of 3.7σ. The combination of fσ_{8} and Planck measurements prefers an even higher growth index of γ=0.639_{-0.025}^{+0.024}, corresponding to a 4.2σ tension with the concordance model. In Planck data, the suppressed growth rate offsets the preference for nonzero curvature and fits the data equally well as the latter model. A higher γ leads to a higher matter fluctuation amplitude S_{8} inferred from galaxy clustering and weak lensing measurements, and a lower S_{8} from Planck data, effectively resolving the S_{8} tension.

The Dark Energy Survey Year 3 high-redshift sample: selection, characterization, and analysis of galaxy clustering

ABSTRACT The fiducial cosmological analyses of imaging surveys like DES typically probe the Universe at redshifts z < 1. We present the selection and characterization of high-redshift galaxy samples using DES Year 3 data, and the analysis of their galaxy clustering measurements. In particular, we use galaxies that are fainter than those used in the previous DES Year 3 analyses and a Bayesian redshift scheme to define three tomographic bins with mean redshifts around z ∼ 0.9, 1.2, and 1.5, which extend the redshift coverage of the fiducial DES Year 3 analysis. These samples contain a total of about 9 million galaxies, and their galaxy density is more than 2 times higher than those in the DES Year 3 fiducial case. We characterize the redshift uncertainties of the samples, including the usage of various spectroscopic and high-quality redshift samples, and we develop a machine-learning method to correct for correlations between galaxy density and survey observing conditions. The analysis of galaxy clustering measurements, with a total signal to noise S/N ∼ 70 after scale cuts, yields robust cosmological constraints on a combination of the fraction of matter in the Universe Ωm and the Hubble parameter h, $\Omega _m h = 0.195^{+0.023}_{-0.018}$, and 2–3 per cent measurements of the amplitude of the galaxy clustering signals, probing galaxy bias and the amplitude of matter fluctuations, bσ8. A companion paper (in preparation) will present the cross-correlations of these high-z samples with cosmic microwave background lensing from Planck and South Pole Telescope, and the cosmological analysis of those measurements in combination with the galaxy clustering presented in this work.

Breaking degeneracies in the first galaxies with clustering

ABSTRACT The high-redshift galaxy UV luminosity function (UVLF) has become essential for understanding the formation and evolution of the first galaxies. Yet, UVLFs only measure galaxy abundances, giving rise to a degeneracy between the mean galaxy luminosity and its stochasticity. Here, we show that upcoming clustering measurements with the JWST, as well as with Roman, will be able to break this degeneracy, even at redshifts z ≳ 10. First, we demonstrate that current Subaru Hyper Suprime-Cam (HSC) measurements of the galaxy bias at z ∼ 4–6 point to a relatively tight halo-galaxy connection, with low stochasticity. Then, we show that the larger UVLFs observed by JWST at z ≳ 10 can be explained with either a boosted average UV emission or an enhanced stochasticity. These two models, however, predict different galaxy biases, which are potentially distinguishable in JWST and Roman surveys. Galaxy-clustering measurements, therefore, will provide crucial insights into the connection between the first galaxies and their dark-matter haloes, and identify the root cause of the enhanced abundance of z ≳ 10 galaxies revealed with JWST during its first year of operations.

Principal component analysis of galaxy clustering in hyperspace of galaxy properties

ABSTRACT Ongoing and upcoming galaxy surveys are providing precision measurements of galaxy clustering. However, a major obstacle in its cosmological application is the stochasticity in the galaxy bias. We explore whether the principal component analysis (PCA) of galaxy correlation matrix in hyperspace of galaxy properties (e.g. magnitude and colour) can reveal further information on mitigating this issue. Based on the hydrodynamic simulation TNG300-1, we analyse the cross-power spectrum matrix of galaxies in the magnitude and colour space of multiple photometric bands. (1) We find that the first principal component $E_i^{(1)}$ is an excellent proxy of the galaxy deterministic bias bD, in that $E_i^{(1)}=\sqrt{P_{mm}/\lambda ^{(1)}}b_{D,i}$. Here, i denotes the i-th galaxy sub-sample. λ(1) is the largest eigenvalue, and Pmm is the matter power spectrum. We verify that this relation holds for all the galaxy samples investigated, down to k ∼ 2h Mpc−1. Since $E_i^{(1)}$ is a direct observable, we can utilize it to design a linear weighting scheme to suppress the stochasticity in the galaxy–matter relation. For an LSST-like magnitude limit galaxy sample, the stochasticity $\mathcal {S}\equiv 1-r^2$ can be suppressed by a factor of $\gtrsim 2$ at k = 1h Mpc−1. This reduces the stochasticity-induced systematic error in the matter power spectrum reconstruction combining galaxy clustering and galaxy-galaxy lensing from $\sim 12~{{\ \rm per\ cent}}$ to $\sim 5~{{\ \rm per\ cent}}$ at k = 1h Mpc−1. (2) We also find that $\mathcal {S}$ increases monotonically with fλ and $f_{\lambda ^2}$. $f_{\lambda ,\lambda ^2}$ quantify the fractional contribution of other eigenmodes to the galaxy clustering and are direct observables. Therefore, the two provide extra information on mitigating galaxy stochasticity.

2D k-th nearest neighbour statistics: a highly informative probe of galaxy clustering

ABSTRACT Beyond standard summary statistics are necessary to summarize the rich information on non-linear scales in the era of precision galaxy clustering measurements. For the first time, we introduce the 2D k-th nearest neighbour (kNN) statistics as a summary statistic for discrete galaxy fields. This is a direct generalization of the standard 1D kNN by disentangling the projected galaxy distribution from the redshift-space distortion signature along the line-of-sight. We further introduce two different flavours of 2D kNNs that trace different aspects of the galaxy field: the standard flavour which tabulates the distances between galaxies and random query points, and a ‘DD’ flavour that tabulates the distances between galaxies and galaxies. We showcase the 2D kNNs’ strong constraining power both through theoretical arguments and by testing on realistic galaxy mocks. Theoretically, we show that 2D kNNs are computationally efficient and directly generate other statistics such as the popular two-point correlation function (2PCF), voids probability function, and counts-in-cell statistics. In a more practical test, we apply the 2D kNN statistics to simulated galaxy mocks that fold in a large range of observational realism and recover parameters of the underlying extended halo occupation distribution (HOD) model that includes velocity bias and galaxy assembly bias. We find unbiased and significantly tighter constraints on all aspects of the HOD model with the 2D kNNs, both compared to the standard 1D kNN, and the classical redshift-space 2PCF.

Toward Accurate Measurement of Property-dependent Galaxy Clustering. II. Tests of the Smoothed Density-corrected V max Method

We present a smoothed density-corrected V max technique for building a random catalog for property-dependent galaxy clustering estimation. This approach is essentially based on the density-corrected V max method of Cole, with three improvements to the original method. To validate the improved method, we generate two sets of flux-limited samples from two independent mock catalogs with different k + e corrections. By comparing the two-point correlation functions, our results demonstrate that the random catalog created by the smoothed density-corrected V max approach provides a more accurate and precise measurement for both sets of mock samples than the commonly used V max and redshift shuffled methods. For the flux-limited samples and color-dependent subsamples, the accuracy of the projected correlation function is well constrained within 1% on the scale of 0.07–30 h −1 Mpc. The accuracy of the redshift-space correlation function is less than 2% as well. Currently, it is the only approach that holds promise for achieving the goal of high-accuracy clustering measures for next-generation surveys.

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . III. Combined cosmological constraints

We present cosmological constraints from the analysis of two-point correlation functions between galaxy positions and galaxy lensing measured in Dark Energy Survey (DES) Year 3 data and measurements of cosmic microwave background (CMB) lensing from the South Pole Telescope (SPT) and Planck. When jointly analyzing the DES-only two-point functions and the DES cross-correlations with SPT+Planck CMB lensing, we find Ωm=0.344±0.030 and S8≡σ8(Ωm/0.3)0.5=0.773±0.016, assuming ΛCDM. When additionally combining with measurements of the CMB lensing autospectrum, we find Ωm=0.306−0.021+0.018 and S8=0.792±0.012. The high signal-to-noise of the CMB lensing cross-correlations enables several powerful consistency tests of these results, including comparisons with constraints derived from cross-correlations only, and comparisons designed to test the robustness of the galaxy lensing and clustering measurements from DES. Applying these tests to our measurements, we find no evidence of significant biases in the baseline cosmological constraints from the DES-only analyses or from the joint analyses with CMB lensing cross-correlations. However, the CMB lensing cross-correlations suggest possible problems with the correlation function measurements using alternative lens galaxy samples, in particular the redmagic galaxies and high-redshift maglim galaxies, consistent with the findings of previous studies. We use the CMB lensing cross-correlations to identify directions for further investigating these problems.8 MoreReceived 27 June 2022Accepted 15 November 2022DOI:https://doi.org/10.1103/PhysRevD.107.023531© 2023 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCosmic microwave backgroundCosmological constantCosmological parametersDark energyDark matterEvolution of the UniverseLarge scale structure of the UniverseOptical, UV, & IR astronomyPhysical SystemsNormal galaxiesGravitation, Cosmology & Astrophysics

Galaxy and Mass Assembly (GAMA)

Aims. We investigate how different mid-infrared (mid-IR) properties of galaxies are correlated with the environment in which the galaxies are located. For this purpose, we first study the dependence of galaxy clustering on the absolute magnitude at 3.4 μm and redshift. Then, we look into the environmental dependence of mid-IR luminosities and the galaxy properties derived from these luminosities. We also explore how various IR galaxy luminosity selections influence the galaxy clustering measurements. Methods. We used a set of W1 (3.4 μm) absolute magnitude (MW1) selected samples from the Galaxy and Mass Assembly (GAMA) survey matched with mid-IR properties from the Wide-field Infrared Survey Explorer (WISE) in the redshift range 0.07 ≤ z < 0.43. We computed the galaxy two-point correlation function (2pCF) and compared the clustering lengths between subsamples binned in MW1 and in redshift. We also measured the marked correlation function (MCF), in which the galaxies are weighted by marks when measuring clustering statistics, using the luminosities in the WISE W1 to W4 (3.4 to 22 μm) bands as marks. Additionally, we compared the measurements of MCFs with different estimates of stellar mass and star formation rate (SFR) used as marks. Finally, we checked how different selections applied to the sample affect the clustering measurements. Results. We show strong clustering dependence on the W1 absolute magnitude: galaxies brighter in the W1 band are more strongly clustered than their fainter counterparts. We also observe a lack of significant redshift dependence of clustering in the redshift range 0.07 ≤ z < 0.43. We show that although the W1 and W2 bands are direct indicators of stellar mass, a galaxy sample selected based on W1 or W2 bands does not perfectly show the clustering behaviour of a stellar mass-selected sample. The proxy relation between W3 and W4 bands and SFR is similar. We also demonstrate the influence of estimation techniques of stellar mass and SFR on the clustering measurements.

Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey

ABSTRACTRecent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here, we investigate three such samplers to motivate and validate the algorithm and settings used for the Dark Energy Survey (DES) analyses of the first 3 yr (Y3) of data from combined measurements of weak lensing and galaxy clustering. We employ the full DES Year 1 likelihood alongside a much faster approximate likelihood, which enables us to assess the outcomes from each sampler choice and demonstrate the robustness of our full results. We find that the ellipsoidal nested sampling algorithm multinest reports inconsistent estimates of the Bayesian evidence and somewhat narrower parameter credible intervals than the sliced nested sampling implemented in polychord. We compare the findings from multinest and polychord with parameter inference from the Metropolis–Hastings algorithm, finding good agreement. We determine that polychord provides a good balance of speed and robustness for posterior and evidence estimation, and recommend different settings for testing purposes and final chains for analyses with DES Y3 data. Our methodology can readily be reproduced to obtain suitable sampler settings for future surveys.

H i HOD. I. The Halo Occupation Distribution of H i Galaxies

The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21 cm radio signals that are emitted from neutral atomic hydrogen (H i) gas will be detected by large-area radio surveys such as the Widefield Australian Square Kilometre Array (SKA) Pathfinder L-band Legacy All-sky Blind Survey and SKA, and deliver galaxy positions and velocities that can be used to measure galaxy clustering statistics. However, to harness this information to improve our cosmological understanding and learn about the physics of dark matter and dark energy, we need to accurately model the manner in which galaxies detected in H i trace the underlying matter distribution of the universe. For this purpose, we develop a new H i-based halo occupation distribution (HOD) model, which makes predictions for the number of galaxies present in dark matter halos conditional on their H i mass. The parameterized HOD model is fit and validated using the Dark Sage semi-analytic model, where we show that the HOD parameters can be modeled by simple linear and quadratic functions of the H i mass. However, we also find that the clustering predicted by the HOD depends sensitively on the radial distributions of the H i galaxies within their host dark matter halos, which does not follow the Navarro–Frenk–White profile in the Dark Sage simulation. As such, this work enables—for the first time—a simple prescription for placing galaxies of different H i masses within dark matter halos in a way that is able to reproduce the H i mass-dependent galaxy clustering and H i mass function simultaneously and without requiring knowledge of the optical properties of the galaxies. Further efforts are required to demonstrate that this model can be used to produce large ensembles of mock galaxy catalogs for upcoming surveys.

Dark Energy Survey year 3 results: Constraints on cosmological parameters and galaxy-bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample

We constrain cosmological and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey Year-3 data. We describe our modeling framework, and choice of scales analyzed, validating their robustness to theoretical uncertainties in small-scale clustering by analyzing simulated data. Using a linear galaxy bias model and redMaGiC galaxy sample, we obtain constraints on the matter density to be $\Omega_{\rm m} = 0.325^{+0.033}_{-0.034}$. We also implement a non-linear galaxy bias model to probe smaller scales that includes parameterization based on hybrid perturbation theory and find that it leads to a 17% gain in cosmological constraining power. We perform robustness tests of our methodology pipeline and demonstrate the stability of the constraints to changes in the theoretical model. Using the redMaGiC galaxy sample as foreground lens galaxies, we find the galaxy clustering and galaxy-galaxy lensing measurements to exhibit significant signals akin to de-correlation between galaxies and mass on large scales, which is not expected in any current models. This likely systematic measurement error biases our constraints on galaxy bias and the $S_8$ parameter. We find that a scale-, redshift- and sky-area-independent phenomenological de-correlation parameter can effectively capture the impact of this systematic error. We trace the source of this de-correlation to a color-dependent photometric issue and minimize its impact on our result by changing the selection criteria of redMaGiC galaxies. Using this new sample, our constraints on the $S_8$ parameter are consistent with previous studies, and we find a small shift in the $\Omega_{\rm m}$ constraints compared to the fiducial redMaGiC sample. We constrain the mean host halo mass of the redMaGiC galaxies in this new sample to be approximately $1.6 \times 10^{13} M_{\odot}/h$.