Search

The redshift-dependent relation between galaxy stellar mass and star formation rate (the Star-Forming Sequence, or SFS) is a key observational yardstick for galaxy assembly. We use the SAGAbg-A sample of background galaxies from the Satellites Around Galactic Analogs (SAGA) Survey to model the low-redshift evolution of the low-mass SFS. The sample is comprised of 23258 galaxies with H$\alpha$-based star formation rates (SFRs) spanning $6<\log_{10}(\rm M_\star/[M_\odot])<10$ and $z<0.21$ ($t<2.5$ Gyr). Although it is common to bin or stack galaxies at $z \lesssim 0.2$ for galaxy population studies, the difference in lookback time between $z=0$ and $z=0.21$ is comparable to the time between $z=1$ to $z=2$. We develop a model to account for both the physical evolution of low-mass SFS and the selection function of the SAGA survey, allowing us to disentangle redshift evolution from redshift-dependent selection effects across the SAGAbg-A redshift range. Our findings indicate significant evolution in the SFS over the last 2.5 Gyr, with a rising normalization: $\langle {\rm SFR}({\rm M_\star=10^{8.5} M_\odot)}\rangle(z)=1.24^{+0.25}_{-0.23}\ {\rm z} -1.47^{+0.03}_{-0.03}$. We also identify the redshift limit at which a static SFS is ruled out at the 95% confidence level, which is $z=0.05$ based on the precision of the SAGAbg-A sample. Comparison with cosmological hydrodynamic simulations reveals that some contemporary simulations under-predict the recent evolution of the low-mass SFS. This demonstrates that the recent evolution of the low-mass SFS can provide new constraints on the assembly of the low-mass Universe and highlights the need for improved models in this regime., Comment: 20 pages, 7 figures. Submitted to ApJ

We investigate the impact of galactic disks on the tidal stripping of cold dark matter subhalos within Milky Way (MW)-mass halos ($M_{\rm vir}\sim 10^{12}\mathrm{M_{\odot}}$) using a new simulation suite, EDEN. By re-simulating 45 MW-mass zoom-in halos from the N-body Symphony compilation with embedded disk potentials, which evolve according to star formation histories predicted by the UniverseMachine model, we self-consistently tie disk growth to halo accretion rate and significantly expand the range of disk masses and formation histories studied. We use the particle-tracking-based subhalo finder Symfind to enhance the robustness of subhalo tracking. We find that disks near the median disk-to-halo mass ratio of our sample ($M_{\ast, \rm Disk}/M_{\rm vir, host} = 2\%$) reduce subhalo peak mass functions within 100 kpc by about $10\%$ for peak masses above $ 10^8\mathrm{M_{\odot}}$. Heavier, MW/M31-like disks ($M_{\ast, \rm Disk}/M_{\rm vir, host} \gtrsim 5\%$) lead to a reduction of more than $40\%$. Subhalo abundance suppression is more pronounced near halo centers, with particularly enhanced stripping for subhalos accreted over 8 Gyr ago on orbits with pericenters < 100 kpc. Suppression is further amplified when disk mass is increased within fixed halo and disk assembly histories. In all cases, the suppression we measure should be interpreted as stripping below the mass resolution limit rather than complete subhalo disruption. This study reshapes our understanding of the MW's impact on its satellites, suggesting it strips subhalos more efficiently than typical MW-mass galaxies due to its larger disk-to-halo mass ratio and earlier disk formation., Comment: 30 pages, 13 figures, 1 table. Submitted to ApJ, comments welcome

We present ~ 126 new spectroscopically identified members of the GD-1 tidal stream obtained with the 5000-fiber Dark Energy Spectroscopic Instrument (DESI). We confirm the existence of a ``cocoon'' which is broad (FWHM~2.932deg~460pc) and kinematically hot (velocity dispersion, sigma~5-8km/s) component that surrounds a narrower (FWHM~0.353deg~55pc) and colder (sigma~ 2.2-2.6km/s) thin stream component (based on a median per star velocity precision of 2.7km/s). The cocoon extends over at least a ~ 20deg segment of the stream observed by DESI. The thin and cocoon components have similar mean values of [Fe/H]: -2.54+/- 0.04dex and -2.45+/-0.06dex suggestive of a common origin. The data are consistent with the following scenarios for the origin of the cocoon. The progenitor of the GD-1 stream was an accreted globular cluster (GC) and: (a) the cocoon was produced by pre-accretion tidal stripping of the GC while it was still inside its parent dwarf galaxy; (b) the cocoon is debris from the parent dwarf galaxy; (c) an initially thin GC tidal stream was heated by impacts from dark subhalos in the Milky Way; (d) an initially thin GC stream was heated by a massive Sagittarius dwarf galaxy; or a combination of some these. In the first two cases the velocity dispersion and mean metallicity are consistent with the parent dwarf galaxy having a halo mass of ~0^9\msun. Future DESI spectroscopy and detailed modeling may enable us to distinguish between these possible origins., Comment: Submitted to ApJ, 23 pages, 13 figures 4 tables

Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the UniverseMachine (UM) galaxy-halo connection framework and the Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place dwarf galaxy star formation and quenching into a cosmological context. UM is a data-driven forward model that flexibly parameterizes galaxy star formation rates (SFR) using only halo mass and assembly history. We add a new quenching model to UM, tailored for galaxies with stellar masses $\lesssim 10^9$ solar masses, and constrain the model down to a stellar mass $\gtrsim 10^7$ solar masses using new SAGA observations of 101 satellite systems around Milky Way (MW)-mass hosts and a sample of isolated field galaxies in a similar mass range from the Sloan Digital Sky Survey (SDSS). The new best-fit model, 'UM-SAGA,' reproduces the satellite stellar mass functions, average SFRs, and quenched fractions in SAGA satellites while keeping isolated dwarfs mostly star forming. The enhanced quenching in satellites relative to isolated field galaxies leads the model to maximally rely on halo assembly to explain the observed environmental quenching. Extrapolating the model down to a stellar mass $\sim 10^{6.5}$ solar masses yields a quenched fraction of $\gtrsim$ 30% for isolated field galaxies and $\gtrsim$ 80% for satellites of MW-mass hosts at this stellar mass. This specific prediction can soon be tested by spectroscopic surveys to reveal the relative importance of internal feedback, cessation of mass and gas accretion, satellite-specific gas processes, and reionization for the evolution of faint low-mass galaxies., Comment: 33 pages, 14 figures, 2 tables. This paper is part of the SAGA Survey Data Release 3. Survey website: https://sagasurvey.org

We present the star-forming properties of 378 satellite galaxies around 101 Milky Way analogs in the Satellites Around Galactic Analogs (SAGA) Survey, focusing on the environmental processes that suppress or quench star formation. In the SAGA stellar mass range of 10^6 to 10^10 solar masses, we present quenched fractions, star-forming rates, gas-phase metallicities, and gas content. The fraction of SAGA satellites that are quenched increases with decreasing stellar mass and shows significant system-to-system scatter. SAGA satellite quenched fractions are highest in the central 100 kpc of their hosts and decline out to the virial radius. Splitting by specific star formation rate (sSFR), the least star-forming satellite quartile follows the radial trend of the quenched population. The median sSFR of star-forming satellites increases with decreasing stellar mass and is roughly constant with projected radius. Star-forming SAGA satellites are consistent with the star formation rate--stellar mass relationship determined in the Local Volume, while the median gas-phase metallicity is higher and median HI gas mass is lower at all stellar masses. We investigate the dependence of the satellite quenched fraction on host properties. Quenched fractions are higher in systems with larger host halo mass, but this trend is only seen in the inner 100 kpc; we do not see significant trends with host color or star formation rate. Our results suggest that lower mass satellites and satellites inside 100 kpc are more efficiently quenched in a Milky Way-like environment, with these processes acting sufficiently slowly to preserve a population of star-forming satellites at all stellar masses and projected radii., Comment: 25 pages, 13 figures, 2 tables. Accepted by ApJ. This paper is part of the SAGA Survey Data Release 3. Data and notebooks are available on the survey website: https://sagasurvey.org

We present the third Data Release (DR3) of the Satellites Around Galactic Analogs (SAGA) Survey, a spectroscopic survey characterizing satellite galaxies around Milky Way (MW)-mass galaxies. The SAGA Survey DR3 includes 378 satellites identified across 101 MW-mass systems in the distance range 25-40.75 Mpc, and an accompanying redshift catalog of background galaxies (including about 46,000 taken by SAGA) in the SAGA footprint of 84.7 sq. deg. The number of confirmed satellites per system ranges from zero to 13, in the stellar mass range 10^6 to 10^10 solar masses. Based on a detailed completeness model, this sample accounts for 94% of the true satellite population down to a stellar mass of 10^7.5 solar masses. We find that the mass of the most massive satellite in SAGA systems is the strongest predictor of satellite abundance; one-third of the SAGA systems contain LMC-mass satellites, and they tend to have more satellites than the MW. The SAGA satellite radial distribution is less concentrated than the MW, and the SAGA quenched fraction below 10^8.5 solar masses is lower than the MW, but in both cases, the MW is within 1 sigma of SAGA system-to-system scatter. SAGA satellites do not exhibit a clear corotating signal as has been suggested in the MW/M31 satellite systems. Although the MW differs in many respects from the typical SAGA system, these differences can be reconciled if the MW is an older, slightly less massive host with a recently accreted LMC/SMC system., Comment: 38 pages, 23 figures, 7 tables. Accepted by ApJ. This paper is part of the SAGA Survey Data Release 3. Data and notebooks will be available on the survey website: https://sagasurvey.org

Strong gravitational lenses are a singular probe of the universe's small-scale structure $\unicode{x2013}$ they are sensitive to the gravitational effects of low-mass $(<10^{10} M_\odot)$ halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraining power of these models has been limited by the methodology or the information content of the data. In this study, we introduce an accelerator-optimized simulation pipeline that can generate lens images with realistic subhalo populations in a matter of milliseconds. Leveraging this simulator, we identify the main methodological limitation of our fiducial SBI analysis: training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to iteratively refine the distribution of simulated training images to better align with the observed data. Using only one-fifth as many mock Hubble Space Telescope (HST) images, SNPE matches the constraints on the low-mass halo population produced by our best non-sequential model. Our experiments suggest that an over three order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. While the full potential of the existing strong lens sample remains to be explored, the notable improvement in constraining power enabled by our sequential approach highlights that the current constraints are limited primarily by methodology and not the data itself. Moreover, our results emphasize the need to treat training set generation and model optimization as interconnected stages of any cosmological analysis using simulation-based inference techniques., Comment: Code available at https://github.com/swagnercarena/paltax

We present Milky Way-est, a suite of 20 cosmological cold-dark-matter-only zoom-in simulations of Milky Way (MW)-like host halos. Milky Way-est hosts are selected such that they (i) are consistent with the MW's measured halo mass and concentration, (ii) accrete a Large Magellanic Cloud (LMC)-like ($\approx 10^{11}~M_{\odot}$) subhalo within the last $2~\mathrm{Gyr}$ on a realistic orbit, placing them near $50~\mathrm{kpc}$ from the host center at $z\approx 0$, and (iii) undergo a $>$1:5 sub-to-host halo mass ratio merger with a Gaia-Sausage-Enceladus (GSE)-like system at early times ($0.67

We investigate the connection between galaxies, dark matter halos, and their large-scale environments with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: Explainable Boosting Machines (EBMs) with simple galaxy environment features and E$(3)$-invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on $3$ Mpc scales. Interpretations via SHapley Additive exPlanations (SHAP) also suggest that, in the context of the TNG300 galaxy--halo connection, simple spherical overdensity on $\sim 3$ Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, $L$, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe ($L = 10$ Mpc). We conclude that $3$ Mpc distance scales are most critical for describing the TNG galaxy--halo connection using the spherical overdensity parameterization but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries., Comment: 20 pages, 9 Figures, submitted to ApJ. Comments welcome!

Measuring the relation between star formation and galactic winds is observationally difficult. In this work we make an indirect measurement of the mass loading factor (the ratio between mass outflow rate and star formation rate) in low-mass galaxies using a differential approach to modeling the low-redshift evolution of the star-forming main sequence and mass-metallicity relation. We use the SAGA (Satellites Around Galactic Analogs) background galaxies, those spectra observed by the SAGA survey that are not associated with the main SAGA host galaxies, to construct a sample of 11925 spectroscopically confirmed low-mass galaxies from $0.01\lesssim z \leq 0.21$ and measure a auroral line metallicity for 120 galaxies. The crux of the method is to use the lowest redshift galaxies as the boundary condition of our model, and to infer a mass-loading factor for the sample by comparing the expected evolution of the low redshift reference sample in stellar mass, gas-phase metallicity, and star formation rate against the observed properties of the sample at higher redshift. We infer a mass-loading factor of $\eta_{\rm m}=0.92^{+1.76}_{-0.74}$, which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different set of assumptions needed for each approach. While our estimate of the mass-loading factor is in good agreement with recent galaxy simulations that focus on resolving the dynamics of the interstellar medium, it is smaller by over an order of magnitude than the mass-loading factor produced by many contemporary cosmological simulations., Comment: 37 pages, 19 figures, accepted to ApJ

The abundance of faint dwarf galaxies is determined by the underlying population of low-mass dark matter (DM) halos and the efficiency of galaxy formation in these systems. Here, we quantify potential galaxy formation and DM constraints from future dwarf satellite galaxy surveys. We generate satellite populations using a suite of Milky Way (MW)--mass cosmological zoom-in simulations and an empirical galaxy--halo connection model, and assess sensitivity to galaxy formation and DM signals when marginalizing over galaxy--halo connection uncertainties. We find that a survey of all satellites around one MW-mass host can constrain a galaxy formation cutoff at peak virial masses of $M_{50}=10^8~M_{\mathrm{\odot}}$ at the $1\sigma$ level; however, a tail toward low $M_{50}$ prevents a $2\sigma$ measurement. In this scenario, combining hosts with differing bright satellite abundances significantly reduces uncertainties on $M_{50}$ at the $1\sigma$ level, but the $2\sigma$ tail toward low $M_{50}$ persists. We project that observations of one (two) complete satellite populations can constrain warm DM models with $m_{\mathrm{WDM}}\approx 10~\mathrm{keV}$ ($20~\mathrm{keV}$). Subhalo mass function (SHMF) suppression can be constrained to $\approx 70\%$, $60\%$, and $50\%$ that in cold dark matter (CDM) at peak virial masses of $10^8$, $10^9$, and $10^{10}~M_{\mathrm{\odot}}$, respectively; SHMF enhancement constraints are weaker ($\approx 20$, $4$, and $2$ times that in CDM, respectively) due to galaxy--halo connection degeneracies. These results motivate searches for faint dwarf galaxies beyond the MW and indicate that ongoing missions like Euclid and upcoming facilities including the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope will probe new galaxy formation and DM physics., Comment: 38 pages, 23 figures, 2 tables. Updated to published version

We explore how dynamical friction in an ultralight dark matter (ULDM) background is affected by dark matter self-interactions. We calculate the force of dynamical friction on a point mass moving through a uniform ULDM background with self-interactions, finding that the force of dynamical friction vanishes for sufficiently strong repulsive self-interactions. Using the pseudospectral solver $\texttt{UltraDark.jl}$, we show with simulations that reasonable values of the ULDM self-interaction strength and particle mass cause $\mathcal{O}(1)$ differences in the acceleration of an object like a supermassive black hole (SMBH) traveling near the center of a soliton, relative to the case with no self-interactions. For example, repulsive self-interactions with $\lambda = 10^{-90}$ yield a deceleration due to dynamical friction $\approx70\%$ smaller than a model with no self-interactions. We discuss the observational implications of our results for SMBHs near soliton centers and for massive satellite galaxies falling into ultralight axion halos and show that outcomes are dependent on whether a self-interaction is present or not., Comment: 17 pages; animations available at https://doi.org/10.5281/zenodo.7927475 or https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW

We present a novel approach to extracting dwarf galaxies from photometric data to measure their average halo mass profile with weak lensing. We characterise their stellar mass and redshift distributions with a spectroscopic calibration sample. Using the ${\sim}5000\mathrm{deg}^2$ multi-band photometry from Dark Energy Survey and redshifts from the Satellites Around Galactic Analogs (SAGA) survey with an unsupervised machine learning method, we select a low-mass galaxy sample spanning redshifts $z{<}0.3$ and divide it into three mass bins. From low to high median mass, the bins contain [146 420, 330 146, 275 028] galaxies and have median stellar masses of $\log_{10}(M_*/M_{\odot})= [8.52^{+0.57}_{-0.76}, 9.02^{+0.50}_ {-0.64}, 9.49^{+0.50}_{-0.58}]$. We measure the stacked excess surface mass density profiles, $\Delta\Sigma(R)$, of these galaxies using galaxy--galaxy lensing with a signal-to-noise of [14, 23, 28]. Through a simulation-based forward-modelling approach, we fit the measurements to constrain the stellar-to-halo mass relation and find the median halo mass of these samples to be $\log_{10}(M_{\rm halo}/M_{\odot})$ = [$10.67\substack{+0.2\\-0.4}$, $11.01\substack{+0.14 \\ -0.27}$,$11.40\substack{+0.08\\-0.15}$]. The CDM profiles are consistent with NFW profiles over scales ${\lesssim}0.15 \rm{h}^{-1}$Mpc. We find that ${\sim}20$ per cent of the dwarf galaxy sample are satellites. This is the first measurement of the halo profiles and masses of such a comprehensive, low-mass galaxy sample. The techniques presented here pave the way for extracting and analysing even lower-mass dwarf galaxies and for more finely splitting galaxies by their properties with future photometric and spectroscopic survey data., Comment: Measurements available at: https://github.com/aamon/Dwarf-Lensing ; 22 pages, 16 figures

Emission line galaxies (ELGs) are now the preeminent tracers of large-scale structure at z>0.8 due to their high density and strong emission lines, which enable accurate redshift measurements. However, relatively little is known about ELG evolution and the ELG-halo connection, exposing us to potential modeling systematics in cosmology inference using these sources. In this paper, we propose a physical picture of ELGs and improve ELG-halo connection modeling using a variety of observations and simulated galaxy models. We investigate DESI-selected ELGs in COSMOS data, and infer that ELGs are rapidly star-forming galaxies with a large fraction exhibiting disturbed morphology, implying that many of them are likely to be merger-driven starbursts. We further postulate that the tidal interactions from mergers lead to correlated star formation in central-satellite ELG pairs, a phenomenon dubbed "conformity." We argue for the need to include conformity in the ELG-halo connection using galaxy models such as IllustrisTNG, and by combining observations such as the DESI ELG auto-correlation, ELG cross-correlation with Luminous Red Galaxies (LRGs), and ELG-cluster cross-correlation. We also explore the origin of conformity using the UniverseMachine model and elucidate the difference between conformity and the well-known galaxy assembly bias effect., Comment: 25 pages, 20 figures, submitted to MNRAS, comments welcome

We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales (<50Mpc/h) with k-th nearest neighbor (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy-halo connection models. Using cross-validation, we identify the galaxy-halo model that ensures both good fits and unbiased predictions across diverse summary statistics. We demonstrate that we can model kNNs effectively down to transverse scales of rp ~ 3Mpc/h and achieve precise and unbiased constraints on the matter density and clustering amplitude, leading to a 2% constraint on sigma_8. Our simulation-based model pipeline is resilient to varied model systematics, spanning simulation codes, halo finding, and cosmology priors. We demonstrate the effectiveness of this approach through an application to the Beyond-2p mock challenge. We propose further explorations to test more complex galaxy-halo connection models and tackle potential observational systematics., Comment: 18 pages, 16 figures, submitted to MNRAS, comments welcome

A major question in $\Lambda$CDM is what this theory actually predicts for the properties of subhalo populations. Subhalos are difficult to simulate and to find within simulations, and this propagates into uncertainty in theoretical predictions for satellite galaxies. We present Symfind, a new particle-tracking-based subhalo finder, and demonstrate that it can track subhalos to orders-of-magnitude lower masses than commonly used halo-finding tools, with a focus on Rockstar and consistent-trees. These longer survival mean that at a fixed peak subhalo mass, we find $\approx 15\%{-}40\%$ more subhalos within the virial radius, $R_\textrm{vir}$, and $\approx 35\%-120\%$ more subhalos within $R_\textrm{vir}/4$ in the Symphony dark-matter-only simulation suite. More subhalos are found as resolution is increased. We perform extensive numerical testing. In agreement with idealized simulations, we show that the $v_{\rm max}$ of subhalos is only resolved at high resolutions ($n_\textrm{peak}\gtrsim3\times 10^4$), but that mass loss itself can be resolved at much more modest particle counts ($n_\textrm{peak}\gtrsim4\times 10^3$). We show that Rockstar converges to false solutions for the mass function, radial distribution, and disruption masses of subhalos. We argue that our new method can trace resolved subhalos until the point of typical galaxy disruption without invoking ``orphan'' modeling. We outline a concrete set of steps for determining whether other subhalo finders meet the same criteria. We publicly release Symfind catalogs and particle data for the Symphony simulation suite at \url{http://web.stanford.edu/group/gfc/symphony}., Comment: 45 pages, 19 figures

The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This opportunity arises in the midst of numerous ground- and space-based surveys that will provide extensive spectroscopy and imaging together covering the entire sky (such as Rubin/LSST, Euclid, UNIONS, SPHEREx, DESI, SDSS-V, GALAH, 4MOST, WEAVE, MOONS, PFS, UVEX, NEO Surveyor, etc.). Roman can uniquely provide uniform high-spatial-resolution (~0.1 arcsec) imaging over the entire sky, vastly expanding the science reach and precision of all of these near-term and future surveys. This imaging will not only enhance other surveys, but also facilitate completely new science. By imaging the full sky over two epochs, Roman can measure the proper motions for stars across the entire Milky Way, probing 100 times fainter than Gaia out to the very edge of the Galaxy. Here, we propose NANCY: a completely public, all-sky survey that will create a high-value legacy dataset benefiting innumerable ongoing and forthcoming studies of the universe. NANCY is a pure expression of Roman's potential: it images the entire sky, at high spatial resolution, in a broad infrared bandpass that collects as many photons as possible. The majority of all ongoing astronomical surveys would benefit from incorporating observations of NANCY into their analyses, whether these surveys focus on nearby stars, the Milky Way, near-field cosmology, or the broader universe., Comment: Submitted to the call for white papers for the Roman Core Community Survey (June 16th, 2023), and to the Bulletin of the AAS

We present the probabilistic stellar mass function (pSMF) of galaxies in the DESI Bright Galaxy Survey (BGS), observed during the One-Percent Survey. The One-Percent Survey was one of DESI's survey validation programs conducted from April to May 2021, before the start of the main survey. It used the same target selection and similar observing strategy as the main survey and successfully observed the spectra and redshifts of 143,017 galaxies in the $r < 19.5$ magnitude-limited BGS Bright sample and 95,499 galaxies in the fainter surface brightness and color selected BGS Faint sample over $z < 0.6$. We derive pSMFs from posteriors of stellar mass, $M_*$, inferred from DESI photometry and spectroscopy using the Hahn et al. (2022a; arXiv:2202.01809) PRObabilistic Value-Added BGS (PROVABGS) Bayesian SED modeling framework. We use a hierarchical population inference framework that statistically and rigorously propagates the $M_*$ uncertainties. Furthermore, we include correction weights that account for the selection effects and incompleteness of the BGS observations. We present the redshift evolution of the pSMF in BGS as well as the pSMFs of star-forming and quiescent galaxies classified using average specific star formation rates from PROVABGS. Overall, the pSMFs show good agreement with previous stellar mass function measurements in the literature. Our pSMFs showcase the potential and statistical power of BGS, which in its main survey will observe >100$\times$ more galaxies. Moreover, we present the statistical framework for subsequent population statistics measurements using BGS, which will characterize the global galaxy population and scaling relations at low redshifts with unprecedented precision., Comment: 25 pages, 12 figures; data used to generate figures is available at https://doi.org/10.5281/zenodo.8018936; submitted to ApJ

We present the first comprehensive Halo Occupation Distribution (HOD) analysis of the DESI One-Percent survey Luminous Red Galaxy (LRG) and Quasi-Stellar Object (QSO) samples. We constrain the HOD of each sample and test possible HOD extensions by fitting the redshift-space galaxy 2-point correlation functions in 0.15 < r < 32 Mpc/h in a set of fiducial redshift bins. We use AbacusSummit cubic boxes at Planck 2018 cosmology as model templates and forward model galaxy clustering with the AbacusHOD package. We achieve good fits with a standard HOD model with velocity bias, and we find no evidence for galaxy assembly bias or satellite profile modulation at the current level of statistical uncertainty. For LRGs in 0.4 < z < 0.6, we infer a satellite fraction of fsat = 11+-1%, a mean halo mass of log10 Mh = 13.40+0.02-0.02, and a linear bias of blin = 1.93+0.06-0.04. For LRGs in 0.6 < z < 0.8, we find fsat = 14+-1%, log10 Mh = 13.24+0.02-0.02, and blin = 2.08+0.03-0.03. For QSOs, we infer fsat = 3+8-2%, log10 Mh = 12.65+0.09-0.04, and blin = 2.63+0.37-0.26 in redshift range 0.8 < z < 2.1. Using these fits, we generate a large suite of high-fidelity galaxy mocks. We also study the redshift-evolution of the DESI LRG sample from z = 0.4 up to z = 1.1, revealing significant and interesting trends in mean halo mass, linear bias, and satellite fraction., Comment: Submitted to MNRAS, comments welcome

We propose that observations of super-massive galaxies contain cosmological constraining power similar to conventional cluster cosmology, and we provide promising indications that the associated systematic errors are comparably easier to control. We consider a fiducial spectroscopic and stellar mass complete sample of galaxies drawn from the Dark Energy Spectroscopic Survey (DESI) and forecast how constraints on Omega_m-sigma_8 from this sample will compare with those from number counts of clusters based on richness. At fixed number density, we find that massive galaxies offer similar constraints to galaxy clusters. However, a mass-complete galaxy sample from DESI has the potential to probe lower halo masses than standard optical cluster samples (which are typically limited to richness above 20 and halo mass above 10^13.5); additionally, it is straightforward to cleanly measure projected galaxy clustering for such a DESI sample, which we show can substantially improve the constraining power on Omega_m. We also compare the constraining power of stellar mass-limited samples to those from larger but mass-incomplete samples (e.g., the DESI Bright Galaxy Survey, BGS, Sample); relative to a lower number density stellar mass-limited samples, we find that a BGS-like sample improves statistical constraints by 60% for Omega_m and 40% for sigma_8, but this uses small scale information which will be harder to model for BGS. Our initial assessment of the systematics associated with supermassive galaxy cosmology yields promising results. The proposed samples have a 10% satellite fraction, but we show that cosmological constraints may be robust to the impact of satellites. These findings motivate future work to realize the potential of super-massive galaxies to probe lower halo masses than richness-based clusters and to avoid persistent systematics associated with optical cluster finding.

The quenched fraction of satellite galaxies is aligned with the orientation of the halo's central galaxy, such that on average, satellites form stars at a lower rate along the major axis of the central. This effect, called anisotropic satellite galaxy quenching (ASGQ), has been found in observational data and cosmological simulations. Analyzing the IllustrisTNG simulation, Mart\'in-Navarro et al. (2021) recently argued that ASGQ is caused by anisotropic energetic feedback and constitutes "compelling observational evidence for the role of black holes in regulating galaxy evolution." In this letter, we study the causes of ASGQ in state-of-the-art galaxy formation simulations to evaluate this claim. We show that cosmological simulations predict that on average, satellite galaxies along the major axis of the dark matter halo tend to have been accreted at earlier cosmic times and are hosted by subhalos of larger peak halo masses. As a result, a modulation of the quenched fraction with respect to the major axis of the central galaxy is a natural prediction of hierarchical structure formation. We show that ASGQ is predicted by the UniverseMachine galaxy formation model, a model without anisotropic feedback. Furthermore, we demonstrate that even in the IllustrisTNG simulation, anisotropic satellite accretion properties are the main cause of ASGQ. Ultimately, we argue that ASGQ is not a reliable indicator of supermassive black hole feedback in galaxy formation simulations and, thus, should not be interpreted as such in observational data., Comment: 7 pages, 4 figures; Submitted to ApJL; Comments welcome!

We present the Cardinal mock galaxy catalogs, a new version of the Buzzard simulation that has been updated to support ongoing and future cosmological surveys, including DES, DESI, and LSST. These catalogs are based on a one-quarter sky simulation populated with galaxies out to a redshift of $z=2.35$ to a depth of $m_{\rm{r}}=27$. Compared to the Buzzard mocks, the Cardinal mocks include an updated subhalo abundance matching (SHAM) model that considers orphan galaxies and includes mass-dependent scatter between galaxy luminosity and halo properties. This model can simultaneously fit galaxy clustering and group--galaxy cross-correlations measured in three different luminosity threshold samples. The Cardinal mocks also feature a new color assignment model that can simultaneously fit color-dependent galaxy clustering in three different luminosity bins. We have developed an algorithm that uses photometric data to improve the color assignment model further and have also developed a novel method to improve small-scale lensing below the ray-tracing resolution. These improvements enable the Cardinal mocks to accurately reproduce the abundance of galaxy clusters and the properties of lens galaxies in the Dark Energy Survey data. As such, these simulations will be a valuable tool for future cosmological analyses based on large sky surveys. The cardinal mock will be released upon publication at https://chunhaoto.com/cardinalsim., Comment: 37 pages; 20 figures. See figure 1 for summary and appendix K for a list of main differences between Cardinal and Buzzard. To be submitted to APJ; Comments welcome. Highlights can be found at https://chunhaoto.com/cardinalsim

We present a novel simulation-based cosmological analysis of galaxy-galaxy lensing and galaxy redshift-space clustering. Compared to analysis methods based on perturbation theory, our simulation-based approach allows us to probe a much wider range of scales, $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, including highly non-linear scales, and marginalises over astrophysical effects such as assembly bias. We apply this framework to data from the Baryon Oscillation Spectroscopic Survey LOWZ sample cross-correlated with state-of-the-art gravitational lensing catalogues from the Kilo Degree Survey and the Dark Energy Survey. We show that gravitational lensing and redshift-space clustering when analysed over a large range of scales place tight constraints on the growth-of-structure parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m} / 0.3}$. Overall, we infer $S_8 = 0.792 \pm 0.022$ when analysing the combination of galaxy-galaxy lensing and projected galaxy clustering and $S_8 = 0.771 \pm 0.027$ for galaxy redshift-space clustering. These findings highlight the potential constraining power of full-scale studies over studies analysing only large scales, and also showcase the benefits of analysing multiple large-scale structure surveys jointly. Our inferred values for $S_8$ fall below the value inferred from the CMB, $S_8 = 0.834 \pm 0.016$. While this difference is not statistically significant by itself, our results mirror other findings in the literature whereby low-redshift large scale structure probes infer lower values for $S_8$ than the CMB, the so-called $S_8$-tension., Comment: 22 pages, 16 figures, submitted to MNRAS, comments welcome

We introduce the DESI LOW-Z Secondary Target Survey, which combines the wide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) with an efficient, low-redshift target selection method. Our selection consists of a set of color and surface brightness cuts, combined with modern machine learning methods, to target low-redshift dwarf galaxies ($z$ < 0.03) between $19 < r < 21$ with high completeness. We employ a convolutional neural network (CNN) to select high-priority targets. The LOW-Z survey has already obtained over 22,000 redshifts of dwarf galaxies (M$_* < 10^9$ M$_\odot$), comparable to the number of dwarf galaxies discovered in SDSS-DR8 and GAMA. As a spare fiber survey, LOW-Z currently receives fiber allocation for just ~50% of its targets. However, we estimate that our selection is highly complete: for galaxies at $z < 0.03$ within our magnitude limits, we achieve better than 95% completeness with ~1% efficiency using catalog-level photometric cuts. We also demonstrate that our CNN selections $z<0.03$ galaxies from the photometric cuts subsample at least ten times more efficiently while maintaining high completeness. The full five-year DESI program will expand the LOW-Z sample, densely mapping the low-redshift Universe, providing an unprecedented sample of dwarf galaxies, and providing critical information about how to pursue effective and efficient low-redshift surveys., Comment: 24 pages, 14 figures, data to reproduce figures: https://zenodo.org/record/7422591

There is untapped cosmological information in galaxy redshift surveys in the non-linear regime. In this work, we use the AEMULUS suite of cosmological $N$-body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales ($0.1-50 \: h^{-1}\,\mathrm{Mpc}$) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics -- the projected correlation function $w_\mathrm{p}(r_\mathrm{p})$, the redshift-space monopole of the correlation function $\xi_0(s)$, and the quadrupole $\xi_2(s)$ -- we emulate statistics that include information about the local environment, namely the underdensity probability function $P_\mathrm{U}(s)$ and the density-marked correlation function $M(s)$. This extends the model of AEMULUS III for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: including $P_\mathrm{U}(s)$ and $M(s)$ improves the precision of our constraints on $\Omega_m$ by 27%, $\sigma_8$ by 19%, and the growth of structure parameter, $f \sigma_8$, by 12% compared to standard statistics. We additionally find that scales below $\sim6 \: h^{-1}\,\mathrm{Mpc}$ contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy-halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration., Comment: Published in the Astrophysical Journal; updated to match journal version

Cosmological and astrophysical observations currently provide the only robust, positive evidence for dark matter. Cosmic probes of dark matter, which seek to determine the fundamental properties of dark matter through observations of the cosmos, have emerged as a promising means to reveal the nature of dark matter. This report summarizes the current status and future potential of cosmic probes to inform our understanding of the fundamental nature of dark matter in the coming decade., Comment: Report of the CF3 Topical Group for Snowmass 2021; 35 pages, 10 figures, many references. V3 updates Fig 3-2 and the author list

We summarize progress made in theoretical astrophysics and cosmology over the past decade and areas of interest for the coming decade. This Report is prepared as the TF09 "Astrophysics and Cosmology" topical group summary for the Theory Frontier as part of the Snowmass 2021 process., Comment: 57 pages

In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at $2

In this white paper, we present an experimental road map for spectroscopic experiments beyond DESI. DESI will be a transformative cosmological survey in the 2020s, mapping 40 million galaxies and quasars and capturing a significant fraction of the available linear modes up to z=1.2. DESI-II will pilot observations of galaxies both at much higher densities and extending to higher redshifts. A Stage-5 experiment would build out those high-density and high-redshift observations, mapping hundreds of millions of stars and galaxies in three dimensions, to address the problems of inflation, dark energy, light relativistic species, and dark matter. These spectroscopic data will also complement the next generation of weak lensing, line intensity mapping and CMB experiments and allow them to reach their full potential., Comment: Contribution to Snowmass 2021

We present Symphony, a compilation of $262$ cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from $10^{11}$$-$$10^{15}~M_{\mathrm{\odot}}$. This compilation includes three existing simulation suites at the cluster and Milky Way$-$mass scales, and two new suites: $39$ Large Magellanic Cloud-mass ($10^{11}~M_{\mathrm{\odot}}$) and $49$ strong-lens-analog ($10^{13}~M_{\mathrm{\odot}}$) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of $\approx 3\times 10^{-7}$ times the host virial mass and a Plummer-equivalent gravitational softening length of $\approx 9\times 10^{-4}$ times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are $\approx 50\%$ higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts' underlying dark matter distributions. We compare our results to the semianalytic model $\mathrm{\texttt{Galacticus}}$, which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We use $\mathrm{\texttt{UniverseMachine}}$ to model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available at http://web.stanford.edu/group/gfc/symphony., Comment: 39 pages, 20 figures, 2 tables. Updated to published version

We present DESI observations of the inner halo of M31, which reveal the kinematics of a recent merger - a galactic immigration event - in exquisite detail. Of the 11,416 sources studied in 3.75 hour of on-sky exposure time, 7,438 are M31 sources with well measured radial velocities. The observations reveal intricate coherent kinematic structure in the positions and velocities of individual stars: streams, wedges, and chevrons. While hints of coherent structures have been previously detected in M31, this is the first time they have been seen with such detail and clarity in a galaxy beyond the Milky Way. We find clear kinematic evidence for shell structures in the Giant Stellar Stream, the Northeast Shelf and Western Shelf regions. The kinematics are remarkably similar to the predictions of dynamical models constructed to explain the spatial morphology of the inner halo. The results are consistent with the interpretation that much of the substructure in the inner halo of M31 is produced by a single galactic immigration event 1 - 2 Gyr ago. Significant numbers of metal-rich stars ([Fe/H]$>-0.5$) are present in all of the detected substructures, suggesting that the immigrating galaxy had an extended star formation history. We also investigate the ability of the shells and Giant Stellar Stream to constrain the gravitational potential of M31, and estimate the mass within a projected radius of 125 kpc to be ${\rm log_{10}}\, M_{\rm NFW}(<125\,{\rm kpc})/M_\odot = 11.80_{-0.10}^{+0.12}$. The results herald a new era in our ability to study stars on a galactic scale and the immigration histories of galaxies., Comment: 45 pages, 22 figures, 8 tables; Astrophysical Journal in press; Data at https://zenodo.org/record/6977494

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory. Over the next 5 yr DESI MWS will observe approximately seven million stars at Galactic latitudes |b|>20 degrees, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines for deriving radial velocities, atmospheric parameters, and chemical abundances. We use ~500,000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to ~1 km/s and [Fe/H] accurate to ~0.2 dex for typical stars in our main sample. We find the stellar parameter distributions from ~100 sq. deg of SV observations with >90% completeness on our main sample are in good agreement with expectations from mock catalogs and previous surveys., Comment: Accepted for publication in ApJ, 44 pages, 25 figures, 4 tables, one of a suite of 8 papers detailing targeting for DESI; v2 added links to data shown in figures, added citations to other DESI papers, corrected author list and minor typos; v3 fixed minor errors in Fig. 6 and clarified associated text; v4 updated to include minor changes in response to review

Over the next five years, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage-IV dark energy galaxy survey. At $z < 0.6$, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the Universe during the dark energy dominated epoch with redshifts of >10 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter., Comment: AJ, submitted, 34 pages, 22 figures, one of a suite of 8 papers detailing targeting for DESI

Ultralight axions (ULAs) are promising dark matter candidates that can have a distinct impact on the formation and evolution of structure on nonlinear scales relative to the cold, collisionless dark matter (CDM) paradigm. However, most studies of structure formation in ULA models do not include the effects of self-interactions, which are expected to arise generically. Here, we study how the tidal evolution of solitons is affected by ULA self-interaction strength and sign. Specifically, using the pseudospectral solver UltraDark.jl, we simulate the tidal disruption of self-interacting solitonic cores as they orbit a $10^{11}~M_{\mathrm{\odot}}$ Navarro-Frenk-White CDM host halo potential for a range of orbital parameters, assuming a fiducial ULA particle mass of $10^{-22}\mathrm{eV}$. We find that repulsive (attractive) self-interactions significantly accelerate (decelerate) soliton tidal disruption. We also identify a degeneracy between the self-interaction strength and soliton mass that determines the efficiency of tidal disruption, such that disruption timescales are affected at the $\sim 50\%$ level for variations in the dimensionless ULA self-coupling from $\lambda=-10^{-92}$ to $\lambda=10^{-92}$., Comment: 15 pages, 10 figures. This paper was made to be as similar to the PRD version as possible

The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights into the origin of the spherically-averaged mass profile of dark matter haloes. We train a gradient-boosted-trees algorithm to predict the final mass profiles of cluster-sized haloes, and measure the importance of the different inputs provided to the algorithm. We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes' Lagrangian patch $R_L$ ($R\sim 0.7\, R_L$) and that in the large-scale environment ($R\sim 1.7~R_L$). The model also identifies three primary time-scales in the halo assembly history that affect the final profile: (i) the formation time of the virialized, collapsed material inside the halo, (ii) the dynamical time, which captures the dynamically unrelaxed, infalling component of the halo over its first orbit, (iii) a third, most recent time-scale, which captures the impact on the outer profile of recent massive merger events. While the inner profile retains memory of the ICs, this information alone is insufficient to yield accurate predictions for the outer profile. As we add information about the haloes' mass accretion history, we find a significant improvement in the predicted profiles at all radii. Our machine-learning framework provides novel insights into the role of the ICs and the mass assembly history in determining the final mass profile of cluster-sized haloes., Comment: 14 pages, 11 figures. Minor changes to match version accepted for publication in MNRAS

We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis. Our hybrid approach first samples the HOD space on a fixed cosmological simulation grid to constrain the high-likelihood region of cosmology+HOD parameter space, and then constructs the emulator within this constrained region. This approach significantly reduces the parameter volume emulated over, thus achieving much smaller emulator errors with fixed number of training points. We demonstrate that this combined with state-of-the-art simulations result in tight emulator errors comparable to expected DESI Y1 LRG sample variance. We leverage the new AbacusSummit simulations and apply our hybrid approach to CMASS non-linear galaxy clustering data. We infer constraints on $\sigma_8 = 0.762\pm0.024$ and $f\sigma_8 (z_{eff} = 0.52) = 0.444\pm0.016$, the tightest among contemporary galaxy clustering studies. We also demonstrate that our $f\sigma_8$ constraint is robust against secondary biases and other HOD model choices, a critical first step towards showcasing the robust cosmology information accessible in non-linear scales. We speculate that the additional statistical power of DESI Y1 should tighten the growth rate constraints by at least another 50-60%, significantly elucidating any potential tension with Planck. We also address the "lensing is low" tension, where we find that the combined effect of a lower $f\sigma_8$ and environment-based bias lowers the predicted lensing signal by 15%, accounting for approximately 50% of the discrepancy between the lensing measurement and clustering-based predictions., Comment: Accepted to MNRAS, comments welcome

We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of $0.1\sim60~h^{-1}$Mpc. Although our simulations are based on $w$CDM with general relativity (GR), we include a scaling parameter of the halo velocity field, $\gamma_f$, defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we find $f\sigma_{8}(z=0.25) = 0.413\pm0.031$, $f\sigma_{8}(z=0.4) = 0.470\pm0.026$ and $f\sigma_{8}(z=0.55) = 0.396\pm0.022$. Compared with Planck observations using a flat $\Lambda$CDM model, our results are lower by $1.9\sigma$, $0.3\sigma$ and $3.4\sigma$ respectively. These results are consistent with other recent simulation-based results at non-linear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of $\gamma_f^{1/2}\sigma_8\approx 0.75$. We note, however, that the BOSS data is well fit assuming GR, i.e. $\gamma_f=1$. We cannot rule out an unknown systematic error in the galaxy bias model at non-linear scales, but near-future data and modeling will enhance our understanding of the galaxy--halo connection, and provide a strong test of new physics beyond the standard model., Comment: 30 pages, 23 figures and 4 tables; ApJ accepted, updated to the match the accepted version

The non-linear process of cosmic structure formation produces gravitationally bound overdensities of dark matter known as halos. The abundances, density profiles, ellipticities, and spins of these halos can be tied to the underlying fundamental particle physics that governs dark matter at microscopic scales. Thus, macroscopic measurements of dark matter halos offer a unique opportunity to determine the underlying properties of dark matter across the vast landscape of dark matter theories. This white paper summarizes the ongoing rapid development of theoretical and experimental methods, as well as new opportunities, to use dark matter halo measurements as a pillar of dark matter physics., Comment: White paper submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021). 88 pages, 9 figures. Comments welcome

Establishing that Vera C. Rubin Observatory is a flagship dark matter experiment is an essential pathway toward understanding the physical nature of dark matter. In the past two decades, wide-field astronomical surveys and terrestrial laboratories have jointly created a phase transition in the ecosystem of dark matter models and probes. Going forward, any robust understanding of dark matter requires astronomical observations, which still provide the only empirical evidence for dark matter to date. We have a unique opportunity right now to create a dark matter experiment with Rubin Observatory Legacy Survey of Space and Time (LSST). This experiment will be a coordinated effort to perform dark matter research, and provide a large collaborative team of scientists with the necessary organizational and funding supports. This approach leverages existing investments in Rubin. Studies of dark matter with Rubin LSST will also guide the design of, and confirm the results from, other dark matter experiments. Supporting a collaborative team to carry out a dark matter experiment with Rubin LSST is the key to achieving the dark matter science goals that have already been identified as high priority by the high-energy physics and astronomy communities., Comment: Contribution to Snowmass 2021

Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8--50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than US $\$300,000$ on energy costs, while simultaneously reducing $\rm{CO}_2$ emission by $2,400$ tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network surrogate models, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reproduces the first-year DES (DES Y1) cosmological constraints derived from a variety of different data vectors with our default code settings, without needing to retune the algorithm every time. Further, we find that LINNA is sufficient for enabling accurate and efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly available at https://github.com/chto/linna, to enable others to perform fast and accurate posterior inference in contemporary cosmological analyses., Comment: 21 pages, 12 figures, submitted to JCAP, comments are welcome

Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak lensing mass calibration. We investigate the selection bias of the stacked cluster lensing signal associated with optically selected clusters, using clusters identified by the redMaPPer algorithm in the Buzzard simulations as a case study. We find that at a given cluster halo mass, the residuals of redMaPPer richness and weak lensing signal are positively correlated. As a result, for a given richness selection, the stacked lensing signal is biased high compared with what we would expect from the underlying halo mass probability distribution. The cluster lensing selection bias can thus lead to overestimated mean cluster mass and biased cosmology results. We show that the lensing selection bias exhibits a strong scale-dependence and is approximately 20 to 60 percent for $\Delta\Sigma$ at large scales. This selection bias largely originates from spurious member galaxies within +/- 20 to 60 Mpc/h along the line of sight, highlighting the importance of quantifying projection effects associated with the broad redshift distribution of member galaxies in photometric cluster surveys. While our results qualitatively agree with those in the literature, accurate quantitative modelling of the selection bias is needed to achieve the goals of cluster lensing cosmology and will require synthetic catalogues covering a wide range of galaxy-halo connection models., Comment: 16 pages, 16 figures; replaced to match published version

Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos ($<10^{10} M_\odot$) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated datasets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys., Comment: Code available at https://github.com/swagnercarena/paltas

Counts of galaxy clusters offer a high-precision probe of cosmology, but control of systematic errors will determine the accuracy of this measurement. Using Buzzard simulations, we quantify one such systematic, the triaxiality distribution of clusters identified with the redMaPPer optical cluster finding algorithm, which was used in the Dark Energy Survey Year-1 (DES Y1) cluster cosmology analysis. We test whether redMaPPer selection biases the clusters' shape and orientation and find that it only biases orientation, preferentially selecting clusters with their major axes oriented along the line of sight. Modeling the richness-mass relation as a log-linear relation, we find that the log-richness amplitude $\ln(A)$ is boosted from the lowest to highest orientation bin with a significance of $14\sigma$, while the orientation dependence of the richness-mass slope and intrinsic scatter is minimal. We also find that the weak lensing shear-profile ratios of cluster-associated dark halos in different orientation bins resemble a "bottleneck" shape that can be quantified with a Cauchy function. We test the correlation of orientation with two other leading systematics in cluster cosmology -- miscentering and projection -- and find a null correlation. Analytic templates for the triaxiality bias of observed-richness and lensing profiles are mapped as corrections to the observable of richness-binned lensing profiles for redMaPPer clusters. The resulting mass bias confirms the DES Y1 finding that triaxiality is a leading source of bias in cluster cosmology. However, the richness-dependence of the bias confirms that triaxiality does not fully resolve the tension at low-richness between DES Y1 cluster cosmology and other probes. Our model can be used for quantifying the impact of triaxiality bias on cosmological constraints for upcoming weak lensing surveys of galaxy clusters.

Cosmological $N$-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that EZmock (Effective Zel'dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the EZmock bias model., Comment: 9 pages, 7 figures; in v2 we incorporate minor modifications from peer review and copyediting

We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA), a method for identifying low-$z$ galaxies on the basis of optical imaging, and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify $z < 0.03$ galaxies from more distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of $> 100,000$ CNN-selected low-$z$ galaxies, we identify $>20,000$ probable satellites located between 36-300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar mass range $9.5 < \log(M_\star/M_\odot) < 11$. We characterize the incompleteness and contamination for CNN-selected samples, and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36-300 kpc does not depend strongly on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations, and highlight the promise of using machine learning for extending galaxy samples of wide-area surveys., Comment: 27 pages, 18 figures, 1 table. Accepted to ApJ. Code available at https://github.com/jwuphysics/xsaga

We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use Halo Occupation Distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with datasets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Field Theory (HEFT) is used as a field-level forward model to describe the full statistical distribution of these tracer samples, and their stochastic power spectra are directly measured and compared to the Poisson shot-noise prediction. While all of the galaxy samples we consider are hosted within haloes with sub-Poisson stochasticity, we observe that the galaxy samples themselves possess stochasticities that range from sub-Poisson to super-Poisson, in agreement with predictions from the halo model. As an application of our methodology, we place priors on the expected degree of non-Poisson stochasticity in cosmological analyses using such samples. We expect these priors will be useful in reducing the complexity of the full parameter space for future analyses using second-order Lagrangian bias models. More generally, the techniques outlined here present the first application of hybrid EFT methods to characterize models of the galaxy--halo connection at the field level, revealing new connections between once-disparate modelling frameworks., Comment: 16 pages, 10 figures. Revised version accepted to MNRAS. Revisions include a new appendix on the covariance of the field-level bias estimator

We present the current state of models for the $z\sim3$ carbon monoxide (CO) line-intensity signal targeted by the CO Mapping Array Project (COMAP) Pathfinder in the context of its early science results. Our fiducial model, relating dark matter halo properties to CO luminosities, informs parameter priors with empirical models of the galaxy-halo connection and previous CO(1-0) observations. The Pathfinder early science data spanning wavenumbers $k=0.051$-$0.62\,$Mpc$^{-1}$ represent the first direct 3D constraint on the clustering component of the CO(1-0) power spectrum. Our 95% upper limit on the redshift-space clustering amplitude $A_{\rm clust}\lesssim70\,\mu$K$^2$ greatly improves on the indirect upper limit of $420\,\mu$K$^2$ reported from the CO Power Spectrum Survey (COPSS) measurement at $k\sim1\,$Mpc$^{-1}$. The COMAP limit excludes a subset of models from previous literature, and constrains interpretation of the COPSS results, demonstrating the complementary nature of COMAP and interferometric CO surveys. Using line bias expectations from our priors, we also constrain the squared mean line intensity-bias product, $\langle{Tb}\rangle^2\lesssim50\,\mu$K$^2$, and the cosmic molecular gas density, $\rho_\text{H2}<2.5\times10^8\,M_\odot\,$Mpc$^{-3}$ (95% upper limits). Based on early instrument performance and our current CO signal estimates, we forecast that the five-year Pathfinder campaign will detect the CO power spectrum with overall signal-to-noise of 9-17. Between then and now, we also expect to detect the CO-galaxy cross-spectrum using overlapping galaxy survey data, enabling enhanced inferences of cosmic star-formation and galaxy-evolution history., Comment: Paper 5 of 7 in series. 17 pages + appendix and bibliography (30 pages total); 15 figures, 6 tables; accepted for publication in ApJ; v3 reflects the accepted version with minor changes and additions to text