Speaker
Description
I will present a pipeline to emulate galaxy clustering statistics at the two-point level and beyond, down to the non-linear regime, including many alternative summary statistics for which no complete analytic models exist in the literature, including the wavelet scattering transform, density-split clustering, Minkowski functionals, void statistics, and more. Our theory models are based on neural networks trained on high-fidelity N-body simulations that meet the requirements to reproduce the properties of the luminous red galaxy sample from the Dark Energy Spectroscopic Instrument. We test the performance of our pipeline at recovering cosmological parameters within the context of the LCDM model and its extensions, and validate its applicability beyond the halo occupation distribution framework that is used to model the connection between dark matter halos and galaxies. We combine the bits of each summary statistic that maximize the Fisher information into a single data vector through a greedy algorithm, which achieves the tightest cosmological constraining power in all cases that are studied.