A forward modeling approach to analyzing galaxy clustering with S im BIG

Abstract

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the S im BIG forward modeling framework. S im BIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply S im BIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ ( k ) , to k max = 0.5 h / Mpc . We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Q uijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of Λ CDM cosmological parameters: Ω m , Ω b , h , n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h / Mpc . This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ∼ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, S im BIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent S im BIG analyses of summary statistics beyond P ℓ .