Stephanie O'Neil

We present the first detailed analysis of the effects of dissipative dark matter (DM) on stellar streams. As a concrete example, we generate a cosmological hydrodynamic zoom-in simulation of a Milky Way (MW)-mass galaxy, assuming that the DM consists of cold dark matter (CDM) with a subcomponent (∼6%) of atomic dark matter (ADM). The ADM subcomponent behaves as collisional, efficiently dissipative gas and allows for the formation of dense compact objects that enhance the central density of satellite galaxies, making them more resistant to tidal disruption. We show that stellar streams with stellar mass M _tot,⋆ ≳ 10 ^5.5 M _⊙ form later and exhibit prolonged star formation throughout their evolution, as compared to their CDM counterparts. Changes to star formation history are reflected in the chemical tracks of the stellar stream stars, where the youngest have enhanced [Fe/H] and [Mg/Fe] in the presence of ADM. Furthermore, a population of low-mass satellites with high ADM mass fractions is identified at low pericenter distances, which may affect the population of streams at M _tot,⋆ ≲ 10 ^5.5 M _⊙ . The results of this study should generalize to other DM models that lead to inner-density enhancements in satellites, such as elastic self-interacting DM in the gravothermal collapse regime. Animations of our simulations are available here ( https://www.youtube.com/playlist?list=PL5IS42ggFs2iFXRQ8uSj1ZfRUGwdibxMM ).

We present a detailed comparison of a Local Group dwarf galaxy analogue evolved in two cosmological models: the standard cold dark matter (DM) and a self-interacting dark matter (SIDM) model with a velocity-dependent cross section. Both simulations are run with the high-resolution, hydrodynamical LYRA galaxy formation model, allowing us to explore the global and substructure properties of the dwarf in a consistent context. While the overall halo growth, final mass, and subhalo mass functions remain largely unchanged across models, SIDM produces a central DM core extending to ∼1 kpc, which does not significantly vary with the inclusion of baryons. Baryonic properties, however, differ notably. The SIDM model leads to a 25% reduction in stellar mass and retains more gas within the stellar half-mass–radius due to a prolonged quiescent phase in star formation. The stellar distribution is less centrally concentrated, and a population of in situ star clusters form at late times. Substructure analysis reveals fewer luminous satellites and more stellar-only systems in SIDM, driven in part by tidal stripping that affects the DM more than the stars. A subset of satellites undergoes tidal-triggered core collapse after infall, enhancing the diversity of SIDM satellite rotation curves. These differences offer potential observational signatures of SIDM in low-mass galaxies.

We analyze the properties of satellite galaxies around 1024 Milky Way–mass hosts from the DREAMS Project, simulated within a ΛCDM cosmology. Utilizing the TNG galaxy-formation model, the DREAMS simulations incorporate both baryonic physics and cosmological uncertainties for a large sample of galaxies with diverse environments and formation histories. We investigate the relative impact of the physical uncertainty from the galaxy-formation model on predicted satellite properties using four metrics: the satellite stellar mass function, radial distribution, inner slope of the dark matter density profile, and stellar half-light radius. We compare these predictions to observations from the SAGA survey and the DREAMS N -body simulations and find that uncertainties from baryonic physics modeling are subdominant to the scatter arising from halo-to-halo variance. Where baryonic modeling does affect satellites, the supernova wind energy has the largest effect on the satellite properties that we investigate. Specifically, increased supernova wind energy suppresses the stellar mass of satellites and results in more extended stellar half-light radii. The adopted wind speed has only a minor impact, and other astrophysical and cosmological parameters show no measurable effect. Our findings highlight the robustness of satellite properties against uncertainties in baryonic physics modeling.

Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. By pairing halo finder and postprocessing codes, merger series in cosmological simulations can be conveniently represented as a graph-like “tree” structure. Previous work has shown that these merger trees are sensitive to cosmological simulation parameters, but as structures comprised of DM halos, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer warm dark matter (WDM) particle masses from the DREAMS simulation suite. We organize the merger trees from 1024 zoom-in simulations into graphs with nodes representing halos at different epochs and edges denoting hereditary links. We vary the complexity of the node features included in the graphs, ranging from a single node feature up through an array of several galactic properties. We train a graph neural network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle, with success depending on the graph complexity and node features. We extend the same methods to supernovae (SNe) and active galactic nuclei feedback parameters, successfully inferring the SNe parameters. With reduced accuracy, the GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.

Dark matter direct detection experiments require information about the local dark matter speed distribution to produce constraints on dark matter candidates, or infer their properties in the event of a discovery. In this paper, we analyze how the uncertainty in the dark matter speed distribution near the Sun is affected by baryonic feedback, halo-to-halo variance, and halo mass. To do so, we harness the statistical power of the new DREAMS cold dark matter simulation suite, which is comprised of 1024 zoom-in Milky Way–mass halos with varied initial conditions as well as cosmological and astrophysical parameters. Applying a normalizing flows emulator to these simulations, we find that the uncertainty in the local dark matter speed distribution is dominated by halo-to-halo variance and, to a lesser extent, uncertainty in host halo mass. Uncertainties in supernova and black hole feedback (from the IllustrisTNG model in this case) are negligible in comparison. Using the DREAMS suite, we present a state-of-the-art prediction for the dark matter speed distribution in the Milky Way. Although the standard halo model is contained within the uncertainty of this prediction, individual galaxies may have distributions that differ from it. Lastly, we apply our DREAMS results to the XENON1T experiment and demonstrate that the astrophysical uncertainties are comparable to the experimental ones, solidifying previous results in the literature obtained with a smaller sample of simulated Milky Way–mass halos.

In this work, we utilize a new suite of Milky Way–mass halos from the DREAMS Project, simulated with cold dark matter (CDM), to quantify the influence of baryon feedback and intrinsic halo-to-halo variance on dark matter density profiles. Our suite of 1024 halos varies over supernova and black hole feedback parameters from the IllustrisTNG model, as well as variations in two cosmological parameters. We find that, for the DREAMS parameter variations, Milky Way–mass dark matter density profiles in the IllustrisTNG model are largely insensitive to astrophysics and cosmology variations, with the dominant source of scatter instead arising from halo-to-halo variance. However, most of the (comparatively minor) feedback-driven variations come from the changes to supernova prescriptions. By comparing to dark-matter-only simulations, we find that the strongest supernova wind energies are so effective at preventing galaxy formation that the halos are nearly entirely collisionless dark matter. Finally, regardless of physics variation, all of the DREAMS halos are roughly consistent with a halo contracting adiabatically from the presence of baryons, unlike models that have bursty stellar feedback. This work represents a step toward assessing the uncertainty in Milky Way dark matter profiles, with direct implications for dark matter searches where systematic uncertainty in the density profile remains a major challenge.

The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from halo occupation distribution (HOD) to empirical and semianalytic models to hydrodynamics. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on the variational diffusion model and transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar–halo mass relations, concentration–mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong-lensing studies.