Galaxy clustering multi-scale emulation

Abstract

Simulation-based inference has seen increasing interest in the past few years as a promising approach to modelling the non-linear scales of galaxy clustering. The common approach, using the Gaussian process, is to train an emulator over the cosmological and galaxy--halo connection parameters independently for every scale. We present a new Gaussian process model that allows the user to extend the input parameter space dimensions and to use a non-diagonal noise covariance matrix. We use our new framework to simultaneously emulate every scale of the non-linear clustering of galaxies in redshift space from the AbacusSummit N-body simulations at redshift $z=0.2$. The model includes nine cosmological parameters, five halo occupation distribution (HOD) parameters, and one scale dimension. Accounting for the limited resolution of the simulations, we train our emulator on scales from $0.3 to $60 and compare its performance with the standard approach of building one independent emulator for each scale. The new model yields more accurate and precise constraints on cosmological parameters compared to the standard approach. As our new model is able to interpolate over the scale space, we are also able to account for the Alcock-Paczynski distortion effect, leading to more accurate constraints on the cosmological parameters.