We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new SIMBIG forward modeling framework. SIMBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small non-linear scales, inaccessible to standard analyses. In this work, we apply SIMBIG to the BOSS CMASS galaxy sample and analyze the power spectrum multipoles up to kmax=0.5h/Mpc. We construct 20,000 simulated galaxy samples using our forward model, which is based on high-resolution QUIJOTE-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 LCDM cosmological parameters: Omega_m, Omega_b, h, n_s, sigma8. We derive significant constraints on Omega_m and sigma8, which are consistent with previous works. Our constraints on sigma8 are 27% more precise than standard analyses. This improvement is equivalent to the statistical gain expected from analyzing a galaxy sample that is nearly 60% larger than CMASS with standard methods. It results from additional cosmological information on non-linear scales beyond the limit of current analytic models, k > 0.25 h/Mpc. While we focus on power spectrum multipoles in this work for validation and comparison to the literature, SIMBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SIMBIG analyses of summary statistics beyond power spectrum.
Département de Physique Théorique
Université de Genève
24, quai Ernest Ansermet
1211 Genève 4
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