Speaker
Description
Speaker: Brooks Thomas
Abstract: One effective way of learning about the production and properties of dark matter in the early universe is by extracting information about the primordial dark-matter phase-space distribution from the matter power spectrum. Recently a simple empirical formula was introduced which is capable of reproducing most of the salient features of the dark-matter phase-space distribution — even in situations in which this distribution is non-thermal, multi-modal, or exhibits other complicated features. In this talk, I examine the extent to which machine-learning techniques can improve upon this analytic approach and demonstrate that these techniques not only succeed in reconstructing the dark-matter phase-space distribution with greater accuracy, but are also applicable to a broader range of matter power spectra.