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Abstract We present cosmological dark matter (DM)–only zoom-in simulations of a Milky Way analog originating from enhanced linear matter power spectraP(k) relative to the standard cold, collisionless DM (CDM) cosmology. We consider a Gaussian power excess inP(k) followed by a cutoff in select cases; this behavior could arise from early-Universe physics that alters the primordial matter power spectrum or DM physics in the radiation-dominated epoch. We find that enhanced initial conditions (ICs) lead to qualitative differences in substructure relative to CDM. In particular, the subhalo mass function (SHMF) resulting from ICs with both an enhancement and a cutoff is amplified at high masses and suppressed at low masses, indicating that DM substructure is sensitive to features inP(k). Critically, the amplitude and shape of the SHMF enhancement depend on the wavenumber of theP(k) excess and the presence or absence of a cutoff on smaller scales. These alterations to the SHMF are mainly imprinted at infall rather than during tidal evolution. Additionally, subhalos are found systematically closer to the host center, and their concentrations are increased in scenarios withP(k) enhancement. Our work thus reveals effects that must be captured to enableP(k) reconstruction using DM substructure. 

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$= 0 mag and $r_{1/2}$= 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~7-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST. 

ABSTRACT In this study, we modify the semi-analytic model galacticus in order to accurately reproduce the observed properties of dwarf galaxies in the Milky Way. We find that reproducing observational determinations of the halo occupation fraction and mass–metallicity relation for dwarf galaxies requires us to include H2 cooling, an updated ultraviolet background radiation model, and to introduce a model for the metal content of the intergalactic medium. By fine-tuning various model parameters and incorporating empirical constraints, we have tailored the model to match the statistical properties of Milky Way dwarf galaxies, such as their luminosity function and size–mass relation. We have validated our modified semi-analytic framework by undertaking a comparative analysis of the resulting galaxy–halo connection. We predict a total of $$300 ^{+75} _{-99}$$ satellites with an absolute V-band magnitude (MV) less than 0 within 300 kpc from our Milky Way analogues. The fraction of subhaloes that host a galaxy at least this bright drops to 50 per cent by a halo peak mass of ∼8.9 × 107 M⊙, consistent with the occupation fraction inferred from the latest observations of Milky Way satellite population. 

Abstract 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 (<10¹⁰M_⊙) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large data sets 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 data sets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.