Speaker
Description
Atomic dark matter (ADM) models, with a minimal content of a dark proton, dark electron, and a massless dark photon, are motivated by theories such as Mirror Twin Higgs. ADM models might address the seeming tension between cold dark matter (CDM) and observations at small scales: excessive number of dwarf galaxies in the Milky Way, and the cuspiness of galactic cores. ADM has been shown to suppress matter perturbations on small scales. N-body simulations with percent ADM subcomponent predict interesting sub-galactic structures. We use similar N-body simulations and Lyman-alpha forest data, which is sensitive to small-scale ADM effects, to produce robust constraints on ADM parameter space. We use machine learning methods to optimize computational efficiency when scanning over the parameter space.
