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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 (≈10¹¹M_⊙) subhalo within the last 2 Gyr on a realistic orbit, placing them near 50 kpc from the host center atz≈ 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 <z< 3). Hosts satisfying these LMC and GSE constraints constitute <1% of all halos in the MW’s mass range, and their total masses grow rapidly at late times due to LMC analog accretion. Compared to hosts of a similar final halo mass that are not selected to include LMC and GSE analogs, Milky Way-est hosts contain 22% more subhalos with present-day virial masses above 10⁸M_⊙throughout the virial radius, on average. This enhancement reaches ≈80% in the inner 100 kpc and is largely, if not entirely, due to LMC-associated subhalos. These systems also induce spatial anisotropy in Milky Way-est subhalo populations, with ≈60% of the total subhalo population within 100 kpc found in the current direction of the LMC. Meanwhile, we find that GSE-associated subhalos do not significantly contribute to present-day Milky Way-est subhalo populations. These results provide context for our Galaxy’s dark matter structure and subhalo population and will help interpret a range of measurements that are currently only possible in the MW.

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${\mathcal{M}}_{50}={10}^8{M}_{\odot }$at the 1σlevel; however, a tail toward low${\mathcal{M}}_{50}$prevents a 2σmeasurement. In this scenario, combining hosts with differing bright satellite abundances significantly reduces uncertainties on${\mathcal{M}}_{50}$at the 1σlevel, but the 2σtail toward low${\mathcal{M}}_{50}$persists. We project that observations of one (two) complete satellite populations can constrain warm DM models withm_WDM≈ 10 keV (20 keV). Subhalo mass function (SHMF) suppression can be constrained to ≈70%, 60%, and 50% that in cold dark matter (CDM) at peak virial masses of 10⁸, 10⁹, and 10¹⁰M_⊙, respectively; SHMF enhancement constraints are weaker (≈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.

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 the 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 Satellites Around Galactic Analogs (SAGA) background galaxies, i.e., spectra observed by the SAGA Survey that are not associated with the main SAGA host galaxies, to construct a sample of 11,925 spectroscopically confirmed low-mass galaxies from 0.01 ≲z≤ 0.21 and measure auroral line metallicities 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${\eta }_{\mathrm{m}}={0.92}_{-0.74}^{+1.76}$, which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different sets 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.

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 ∼ 60h⁻¹Mpc. Although our simulations are based onwCDM with general relativity (GR), we include a scaling parameter of the halo velocity field,γ_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 findfσ₈(z= 0.25) = 0.413 ± 0.031,fσ₈(z= 0.4) = 0.470 ± 0.026, andfσ₈(z= 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1.9σ, 0.3σ, and 3.4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear 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.,γ_f= 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear 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.

Standard halo occupation distribution (HOD) models predict how galaxies occupy halos based on a single property, the virial mass,M_vir. To incorporate galaxy assembly bias, we consider alternative halo mass proxies to use in the HOD. A promising replacement is maximum circular velocity,$V_{\max }$, which measures the halo’s potential well, and successfully predicts galaxy clustering in subhalo abundance matching models. We fit galaxy clustering measurements from SDSS DR7 with two modified HODs besides the standardM_vir-based model: one where$V_{\max }$is used as the halo property that determines galaxy occupation, and the other adopting an adjustable combination ofM_virand$V_{\max }$. Except for the reduced volumeM_r < − 20 threshold sample fit with$V_{\max }$alone, neither change improves the model performance in predicting the projected two-point correlation function,w_p. We conclude that switching the primary variable to$V_{\max }$does not significantly improve the HOD model’s ability to fit galaxy clustering.

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 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 ∼400 000 Sloan Digital Sky Survey 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 ≤ 17.8 and zspec ≤ 0.4) 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 two-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.

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(NCIC), with the standard measurements of the projected two-point correlation function wp(rp), and number density ngal of galaxies. We measure ngal, wp(rp), and P(NCIC) 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 Mr < −20.0 and Mr < −19.5 samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the Mr < −20.5 and Mr < −19.0 samples. We find no evidence of central assembly bias in the Mr < −21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the Mr < −20.0 and Mr < −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.

We present Symphony, a compilation of 262 cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from 10¹¹–10¹⁵M_⊙. This compilation includes three existing simulation suites at the cluster and Milky Way–mass scales, and two new suites: 39 Large Magellanic Cloud-mass (10¹¹M_⊙) and 49 strong-lens-analog (10¹³M_⊙) 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 ≈3 × 10⁻⁷times the host virial mass and a Plummer-equivalent gravitational softening length of ≈9 × 10⁻⁴times 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 ≈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 modelGalacticus, 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 useUniverseMachineto 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 athttp://web.stanford.edu/group/gfc/symphony.

We introduce the DESI LOW-ZSecondary 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-Zsurvey has already obtained over 22,000 redshifts of dwarf galaxies (M_*< 10⁹M_⊙), comparable to the number of dwarf galaxies discovered in the Sloan Digital Sky Survey DR8 and GAMA. As a spare fiber survey, LOW-Zcurrently receives fiber allocation for just ∼50% of its targets. However, we estimate that our selection is highly complete: for galaxies atz< 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 selectionsz< 0.03 galaxies from the photometric cuts subsample at least 10 times more efficiently while maintaining high completeness. The full 5 yr DESI program will expand the LOW-Zsample, 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.