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Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using thearepocode. One suite consists of uniform-box simulations covering a ${\left(25h^{-1}\mathrm{Mpc}\right)}^3$volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales. 

Abstract Using cosmological hydrodynamical zoom-in simulations, we explore the properties of subhalos in Milky Way analogs that contain a subcomponent of atomic dark matter (ADM). ADM differs from cold dark matter (CDM) due to the presence of self-interactions that lead to energy dissipation, analogous to standard model baryons. This model can arise in dark sectors that are natural and theoretically motivated extensions to the standard model. The simulations used in this work were carried out usingGIZMOand utilize the FIRE-2 galaxy formation physics in the standard model baryonic sector. For the parameter points we consider, the ADM gas cools efficiently, allowing it to collapse to the center of subhalos. This increases a subhalo’s central density and affects its orbit, with more subhalos surviving small pericentric passages. The subset of subhalos that host satellite galaxies have cuspier density profiles and smaller stellar half-mass radii relative to CDM. The entire population of dwarf galaxies produced in the ADM simulations is more compact than those seen in CDM simulations, unable to reproduce the entire diversity of observed dwarf galaxy structures. Additionally, we also identify a population of highly compact subhalos that consist nearly entirely of ADM and form in the central region of the host, where they can leave distinctive imprints in the baryonic disk. This work presents the first detailed exploration of subhalo properties in a strongly dissipative dark matter scenario, providing intuition for how other regions of ADM parameter space, as well as other dark sector models, would impact galactic-scale observables. 

ABSTRACT Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ∼92 million stars. The network, which takes as input a star’s parallax, angular coordinates, and proper motions, is trained and validated on ∼6.4 million stars in Gaia with complete phase-space information. The network’s uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalogue to identify candidate stars that belong to the Milky Way’s most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ∼450 000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network’s predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate, and apply such a neural network when complete observational data is not available. 

Abstract Nyx is a nearby, prograde, and high-eccentricity stellar stream physically contained in the thick disk, but its origin is unknown. Nyx could be the remnant of a disrupted dwarf galaxy, in which case the associated dark matter substructure could affect terrestrial dark matter direct-detection experiments. Alternatively, Nyx could be a signature of the Milky Way’s disk formation and evolution. To determine the origin of Nyx, we obtained high-resolution spectroscopy of 34 Nyx stars using Keck/HIRES and Magellan/MIKE. A differential chemical abundance analysis shows that most Nyx stars reside in a metal-rich ([Fe/H] > −1) high-αcomponent that is chemically indistinguishable from the thick disk. This rules out the originally suggested scenario that Nyx is the remnant of a single massive dwarf galaxy merger. However, we also identify 5 substantially more metal-poor stars ([Fe/H] ∼ −2.0) whose chemical abundances are similar to those of the metal-weak thick disk. It remains unclear how stars that are chemically identical to the thick disk can be on such prograde, high-eccentricity orbits. We suggest two most likely scenarios: that Nyx is the result of an early minor dwarf galaxy merger, or that it is a record of the early spin-up of the Milky Way disk—although neither perfectly reproduces the chemodynamic observations. The most likely formation scenarios suggest that future spectroscopic surveys should find Nyx-like structures outside of the solar neighborhood. 

ABSTRACT We combine the isothermal Jeans model and the model of adiabatic halo contraction into a semi-analytic procedure for computing the density profile of self-interacting dark-matter (SIDM) haloes with the gravitational influence from the inhabitant galaxies. The model agrees well with cosmological SIDM simulations over the entire core-forming stage up to the onset of gravothermal core-collapse. Using this model, we show that the halo response to baryons is more diverse in SIDM than in CDM and depends sensitively on galaxy size, a desirable feature in the context of the structural diversity of bright dwarfs. The fast speed of the method facilitates analyses that would be challenging for numerical simulations – notably, we quantify the SIDM halo response as functions of the baryonic properties, on a fine mesh grid spanned by the baryon-to-total-mass ratio, Mb/Mvir, and galaxy compactness, r1/2/Rvir; we show with high statistical precision that for typical Milky-Way-like systems, the SIDM profiles are similar to their CDM counterparts; and we delineate the regime of core-collapse in the Mb/Mvir − r1/2/Rvir space, for a given cross section and concentration. Finally, we compare the isothermal Jeans model with the more sophisticated gravothermal fluid model, and show that the former yields faster core formation and agrees better with cosmological simulations. We attribute the difference to whether the target CDM halo is used as a boundary condition or as the initial condition for the gravothermal evolution, and thus comment on possible improvements of the fluid model. We have made our model publicly available at https://github.com/JiangFangzhou/SIDM.