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Abstract We present a novel method for systematically assessing the impact of the central potential fluctuations associated with bursty outflows on the structures of dark matter halos for classical and ultrafaint dwarf (UFD) galaxies. Specifically, we use dark-matter-only simulations augmented with a manually added massive particle that modifies the central potential and approximately accounts for a centrally concentrated baryonic component. This approach enables precise control over the magnitude, frequency, and timing of rapid outflow events. We demonstrate that this method can reproduce the established result of core formation for systems that undergo multiple episodes of bursty outflows. In contrast, we also find that equivalent models involving only single (or a small number of) burst episodes do not form cores with the same efficacy. This is important because many UFDs in the local Universe are observed to have tightly constrained star formation histories that are best described by a single early burst of star formation. Using a suite of cosmological zoom-in simulations, we identify the regimes in which single bursts can and cannot form a cored density profile. Our results suggest that it may be difficult to form cores in UFD-mass systems with a single early burst, regardless of its magnitude. 

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 We present deep Hubble Space Telescope photometry of 10 targets from Treasury Program GO-14734, including six confirmed ultrafaint dwarf (UFD) galaxies, three UFD candidates, and one likely globular cluster. Six of these targets are satellites of, or have interacted with, the Large Magellanic Cloud (LMC). We determine their structural parameters using a maximum-likelihood technique. Using our newly derived half-light radius (r_h) andV-band magnitude (M_V) values in addition to literature values for other UFDs, we find that UFDs associated with the LMC do not show any systematic differences from Milky Way UFDs in the magnitude–size plane. Additionally, we convert simulated UFD properties from the literature into theM_V–r_hobservational space to examine the abilities of current dark matter (DM) and baryonic simulations to reproduce observed UFDs. Some of these simulations adopt alternative DM models, thus allowing us to also explore whether theM_V–r_hplane could be used to constrain the nature of DM. We find no differences in the magnitude–size plane between UFDs simulated with cold, warm, and self-interacting DM, but note that the sample of UFDs simulated with alternative DM models is quite limited at present. As more deep, wide-field survey data become available, we will have further opportunities to discover and characterize these ultrafaint stellar systems and the greater low surface-brightness universe. 

ABSTRACT We present a new suite of over 1500 cosmological N-body simulations with varied warm dark matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV with an uncertainty of ±0.5 keV at a 95 per cent confidence level from (25 h−1Mpc)2 maps. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h−1 kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make two times more precise predictions and can make accurate predictions to two times as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment. 

ABSTRACT We present a new set of cosmological zoom-in simulations of a Milky Way (MW)-like galaxy that for the first time include elastic velocity-dependent self-interacting dark matter (SIDM) and IllustrisTNG physics. With these simulations, we investigate the interaction between SIDM and baryons and its effects on the galaxy evolution process. We also introduce a novel set of modified dark matter-only simulations that can reasonably replicate the effects of fully realized hydrodynamics on the DM halo while simplifying the analysis and lowering the computational cost. We find that baryons change the thermal structure of the central region of the halo to a greater extent than the SIDM scatterings for MW-like galaxies. Additionally, we find that the new thermal structure of the MW-like halo causes SIDM to create cuspier central densities rather than cores because the SIDM scatterings remove the thermal support by transferring heat away from the centre of the galaxy. We find that this effect, caused by baryon contraction, begins to affect galaxies with a stellar mass of 108 M⊙ and increases in strength to the MW-mass scale.