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

The stellar masses of galaxies are measured using integrated light via several methods -- however, few of these methods were designed for low-mass ($M_{\star}\lesssim10^{8}\rm{M_{\odot}}$) "dwarf" galaxies, whose properties (e.g., stochastic star formation, low metallicity) pose unique challenges for estimating stellar masses. In this work, we quantify the precision and accuracy at which stellar masses of low-mass galaxies can be recovered using UV/optical/IR photometry. We use mock observations of 469 low-mass galaxies from a variety of models, including both semi-empirical models (GRUMPY, UniverseMachine-SAGA) and cosmological baryonic zoom-in simulations (MARVELous Dwarfs and FIRE-2), to test literature color-$M_\star/L$ relations and multi-wavelength spectral energy distribution (SED) mass estimators. We identify a list of "best practices" for measuring stellar masses of low-mass galaxies from integrated photometry. These include updated prescriptions for stellar mass based on $g-r$ color and WISE 3.4 $\mu$m luminosity, which are less systematically biased than literature calibrations and can recover true stellar masses of low-mass galaxies with $\sim0.1$ dex precision. When using SED fitting to estimate stellar mass, we find that the form of the assumed star formation history can induce significant biases: parametric SFHs can underestimate stellar mass by as much as $\sim0.4$ dex, while non-parametric SFHs recover true stellar masses with insignificant offset ($-0.03\pm0.11$ dex). However, we also caution that non-informative dust attenuation priors may introduce $M_\star$ uncertainties of up to $\sim0.6$ dex., Comment: 28 pages including references, 9 figures; submitted to ApJ

The Milky Way (MW) is surrounded by dozens of satellite galaxies, with six-dimensional (6D) phase space information measured for over 80% of this population. The spatial distribution of these satellites is an essential probe of galaxy formation and for mapping the MW's underlying dark matter distribution. Using measured 6D phase space information of known MW satellites, we calculate orbital histories in a joint MW+LMC potential, including the gravitational influence of the LMC on all satellites, on the MW's center of mass, and dynamical friction owing to both galaxies, to investigate the evolution of the MW's cumulative radial profile. We conclude radial profiles become more concentrated over time when we consider the LMC's gravitational influence and the group infall of LMC-associated satellites. The MW's radial distribution is consistently more concentrated at present-day, 1 Gyr, and 2 Gyr ago compared to recent surveys of nearby MW-like systems. Compared to MW-mass hosts in cosmological, zoom-in simulations, we find the MW's radial profile is also more concentrated than those of simulated counterparts; however, some overlap exists between simulation results and our analysis of the MW's satellite distribution 2 Gyr ago, pre-LMC infall. Finally, we posit radial profiles of simulated MW-mass analogs also hosting an LMC companion are likely to evolve similarly to our results, such that the accretion of a massive satellite along with its satellites will lead to a more concentrated radial profile as the massive satellite advances toward its host galaxy., Comment: 14 pages, 6 figures, accepted to ApJ

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

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

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 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

We report the discovery of two ultra-faint dwarf galaxies, Leo M and Leo K, that lie outside the halo of the Milky Way. Using Hubble Space Telescope imaging of the resolved stars, we create color-magnitude diagrams reaching the old main sequence turn-off of each system and (i) fit for structural parameters of the galaxies; (ii) measure their distances using the luminosity of the Horizontal Branch stars; (iii) estimate integrated magnitudes and stellar masses; and (iv) reconstruct the star formation histories. Based on their location in the Local Group, neither galaxy is currently a satellite of the Milky Way, although Leo K is located ~26 kpc from the low-mass galaxy Leo T and these two systems may have had a past interaction. Leo M and Leo K have stellar masses of 1.8 (+0.3/-0.2) x 10^4 Msun and 1.2+/-0.2 x 10^4 Msun, and were quenched 10.6 (+2.2/-1.1) Gyr and 12.8 (+0.1/-4.2) Gyr ago, respectively. Given that the galaxies are not satellites of the MW, it is unlikely that they were quenched by environmental processing. Instead, given their low stellar masses, their early quenching timescales are consistent with the scenario that a combination of reionization and stellar feedback shut-down star formation at early cosmic times., Comment: 12 pages, 9 figures, 1 table

Within the $\Lambda$CDM cosmology, dark matter haloes are comprised of both a smooth component and a population of smaller, gravitationally bound subhaloes. These components are often treated as a single halo when halo properties, such as density profiles, are extracted from simulations. Recent work has shown that density profiles change substantially when subhalo mass is excluded. In this paper, we expand on this result by analysing the change in three specific host halo properties -- concentration ($c_{\rm{NFW}}$), spin ($\lambda_{\rm Bullock}$), and shape ($c/a$), -- when calculated only from the smooth component of the halo. This analysis is performed on both Milky Way-mass haloes and cluster-mass haloes in high-resolution, zoom-in, $N$-body simulations. We find that when subhaloes are excluded the median value of (1) $c_{\rm{NFW}}$ is enhanced by $\approx 38 \pm 12\%$ and $\approx 88 \pm 7.7\%$ for Milky Way mass ($10^{12.1}\,\text{M}_\odot$) and cluster mass ($10^{14.8}\,\text{M}_\odot$) haloes respectively, (2) $\lambda_{\rm Bullock}$ is reduced for Milky Way mass by $\approx 16 \pm 6.8\%$ and cluster mass haloes by $\approx 32 \pm 8.9\%$. Additionally, with the removal of subhaloes, cluster mass haloes tend to become more spherical as the ratio of minor-to-major axis, $c/a$, increases by $\approx 12 \pm 4\%$, whereas Milky Way mass haloes remain approximately the same shape with $c/a$ changed by $\approx 1.2 \pm 5.6\%$. The fractional change of each of these properties depends primarily on the amount of mass that is removed from the halo system and, to a lesser extent, mass accretion history. Our findings demonstrate that the properties of the smooth components of dark matter haloes are biased relative to the total mass of the halo including subhaloes., Comment: Accepted to MNRAS 09/26/2023

We report the discovery of an ultrafaint dwarf (UFD) galaxy, Pegasus W, located on the far side of the Milky Way-M31 system and outside the virial radius of M31. The distance to the galaxy is 915 (+60/-91) kpc, measured using the luminosity of horizontal branch (HB) stars identified in Hubble Space Telescope optical imaging. The galaxy has a half-light radius (r_h) of 100 (+11/-13) pc, M_V = -7.20 (+0.17/-0.16) mag, and a present-day stellar mass of 6.5 (+1.1/-1.4) x 10^4 Msun. We identify sources in the color-magnitude diagram (CMD) that may be younger than ~500 Myr suggesting late-time star formation in the UFD galaxy, although further study is needed to confirm these are bona fide young stars in the galaxy. Based on fitting the CMD with stellar evolution libraries, Pegasus W shows an extended star formation history (SFH). Using the tau_90 metric (defined as the timescale by which the galaxy formed 90% of its stellar mass), the galaxy was quenched only 7.4 (+2.2/-2.6) Gyr ago, which is similar to the quenching timescale of a number of UFD satellites of M31 but significantly more recent than the UFD satellites of the Milky Way. Such late-time quenching is inconsistent with the more rapid timescale expected by reionization and suggests that, while not currently a satellite of M31, Pegasus W was nonetheless slowly quenched by environmental processes., Comment: 15 pages, 10 figures, 2 tables

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

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 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

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science., Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" workshop

We present observational constraints on the galaxy-halo connection, focusing particularly on galaxy assembly bias, from a novel combination of counts-in-cylinders statistics, $P(N_{\rm{CIC}})$, with the standard measurements of the projected two-point correlation function, $w_{\rm{p}}(r_{\rm{p}})$, and number density, $n_{\rm{gal}}$, of galaxies. We measure $n_{\rm{gal}}$, $w_{\rm{p}}(r_{\rm{p}})$ and $P(N_{\rm{CIC}})$ for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the $M_r<-20.0$ and $M_r<-19.5$ samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the $M_r<-20.5$ and $M_r<-19.0$ samples. We find no evidence of central assembly bias in the $M_r<-21.0$ sample. We observe only a marginal preference for negative satellite assembly bias in the $M_r<-20.0$ and $M_r<-19.0$ samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy-halo connection studies and cosmological analyses., Comment: 16+5 pages, 9+3 figures, 5+2 tables, Fig. 6 shows the main result. To be submitted 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

Despite efforts over several decades, direct-detection experiments have not yet led to the discovery of the dark matter (DM) particle. This has led to increasing interest in alternatives to the Lambda CDM (LCDM) paradigm and alternative DM scenarios (including fuzzy DM, warm DM, self-interacting DM, etc.). In many of these scenarios, DM particles cannot be detected directly and constraints on their properties can ONLY be arrived at using astrophysical observations. The Dark Energy Spectroscopic Instrument (DESI) is currently one of the most powerful instruments for wide-field surveys. The synergy of DESI with ESA's Gaia satellite and future observing facilities will yield datasets of unprecedented size and coverage that will enable constraints on DM over a wide range of physical and mass scales and across redshifts. DESI will obtain spectra of the Lyman-alpha forest out to z~5 by detecting about 1 million QSO spectra that will put constraints on clustering of the low-density intergalactic gas and DM halos at high redshift. DESI will obtain radial velocities of 10 million stars in the Milky Way (MW) and Local Group satellites enabling us to constrain their global DM distributions, as well as the DM distribution on smaller scales. The paradigm of cosmological structure formation has been extensively tested with simulations. However, the majority of simulations to date have focused on collisionless CDM. Simulations with alternatives to CDM have recently been gaining ground but are still in their infancy. While there are numerous publicly available large-box and zoom-in simulations in the LCDM framework, there are no comparable publicly available WDM, SIDM, FDM simulations. DOE support for a public simulation suite will enable a more cohesive community effort to compare observations from DESI (and other surveys) with numerical predictions and will greatly impact DM science., Comment: Contributed white paper to Snowmass 2021, CF03; minor revisions

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

Over the past several decades, unexpected astronomical discoveries have been fueling a new wave of particle model building and are inspiring the next generation of ever-more-sophisticated simulations to reveal the nature of Dark Matter (DM). This coincides with the advent of new observing facilities coming online, including JWST, the Rubin Observatory, the Nancy Grace Roman Space Telescope, and CMB-S4. The time is now to build a novel simulation program to interpret observations so that we can identify novel signatures of DM microphysics across a large dynamic range of length scales and cosmic time. This white paper identifies the key elements that are needed for such a simulation program. We identify areas of growth on both the particle theory side as well as the simulation algorithm and implementation side, so that we can robustly simulate the cosmic evolution of DM for well-motivated models. We recommend that simulations include a fully calibrated and well-tested treatment of baryonic physics, and that outputs should connect with observations in the space of observables. We identify the tools and methods currently available to make predictions and the path forward for building more of these tools. A strong cosmic DM simulation program is key to translating cosmological observations to robust constraints on DM fundamental physics, and provides a connection to lab-based probes of DM physics., Comment: Contribution to Snowmass 2021

Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time (LSST) are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on $ugriz$ images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of $\sim$400,000 Sloan Digital Sky Survey (SDSS) galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets ($r\leq17.8$ and $z_{\mathrm{spec}}\leq0.4$) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a 2-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g., size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g., magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1 ., Comment: 22 pages, 12 figures. Published in MNRAS

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