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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. 

The distribution of offsets between the brightest cluster galaxies of galaxy clusters and the centroid of their dark matter distributions is a promising probe of the underlying dark matter physics. In particular, since this distribution is sensitive to the shape of the potential in galaxy cluster cores, it constitutes a test of dark matter self-interaction on the largest mass scales in the universe. We examine these offsets in three suites of modern cosmological simulations; IllustrisTNG, MillenniumTNG and BAHAMAS. For clusters above , we examine the dependence of the offset distribution on gravitational softening length, the method used to identify centroids, redshift, mass, baryonic physics, and establish the stability of our results with respect to various nuisance parameter choices. We find that offsets are overwhelmingly measured to be smaller than the minimum converged length scale in each simulation, with a median offset of in the highest resolution simulation considered, TNG300-1, which uses a gravitational softening length of . We also find that centroids identified via source extraction on smoothed dark matter and stellar particle data are consistent with the potential minimum, but that observationally relevant methods sensitive to cluster strong gravitational lensing scales, or those using the the “light traces mass” approach, in this context meaning gas is used as a tracer for the potential, can overestimate offsets by factors of $\sim 10$and $\sim 30$, respectively. This has the potential to reduce tensions with existing offset measurements which have served as evidence for a nonzero dark matter self-interaction cross section.