SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering

Abstract

We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power spectrum, P_, with analytic models based on perturbation theory. Consequently, they do not fully exploit the non-linear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the SimBIG forward modelling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with stochastic weight averaging to perform massive data compression of the galaxy field. We infer constraints on _m = 0.267^+0.033_-0.029 and _8=0.762^+0.036_-0.035. While our constraints on _m are in-line with standard P_ analyses, those on _8 are 2.65 tighter. Our analysis also provides constraints on the Hubble constant H_0=64.5 3.8 \ km / s / Mpc from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with P_. We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid.

Tasks

Reproductions