$\mathrm{S}\mathrm{IM}\mathrm{BIG}$: A Forward Modeling Approach To Analyzing Galaxy Clustering

Abstract

We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new $\mathrm{S}\mathrm{IM}\mathrm{BIG}$ forward modeling framework. $\mathrm{S}\mathrm{IM}\mathrm{BIG}$ leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small non-linear scales, inaccessible with standard analyses. In this work, we apply $\mathrm{S}\mathrm{IM}\mathrm{BIG}$ to the BOSS CMASS galaxy sample and analyze the power spectrum, $P_{\ell }(k)$, to $k_{\max }=0.5h/\mathrm{Mpc}$. We construct 20,000 simulated galaxy samples using our forward model, which is based on high-resolution $\mathrm{Q}\mathrm{UIJOTE}$ $N$-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 $\Lambda$CDM cosmological parameters: ${\Omega }_m,{\Omega }_b,h,n_s,{\sigma }_8$. We derive significant constraints on ${\Omega }_m$ and ${\sigma }_8$, which are consistent with previous works. Our constraints on ${\sigma }_8$ are $27\%$ more precise than standard analyses. This improvement is equivalent to the statistical gain expected from analyzing a galaxy sample that is $\sim 60\%$ larger than CMASS with standard methods. It results from additional cosmological information on non-linear scales beyond the limit of current analytic models, $k>0.25h/\mathrm{Mpc}$. While we focus on $P_{\ell }$ in this work for validation and comparison to the literature, $\mathrm{S}\mathrm{IM}\mathrm{BIG}$ provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent $\mathrm{S}\mathrm{IM}\mathrm{BIG}$ analyses of summary statistics beyond $P_{\ell }$.

9 pages, 5 figures