({\rm S{\scriptsize IM}BIG}\): A Forward Modeling Approach To Analyzing Galaxy Clustering

• Published in: arXiv.org 2022-11
• Main Authors: Hahn, ChangHoon, Eickenberg, Michael, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Bruno Régaldo-Saint Blancard, Abidi, Muntazir M
• Format: Article
• Language: English
• Subjects: Astronomical models; Clustering; Constraint modelling; Galaxies; Inference; Samples; Simulation; Statistical analysis
• ISSN: 2331-8422

We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new \({\rm S{\scriptsize IM}BIG}\) forward modeling framework. \({\rm S{\scriptsize IM}BIG}\) 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 \({\rm S{\scriptsize IM}BIG}\) to the BOSS CMASS galaxy sample and analyze the power spectrum, \(P_\ell(k)\), to \(k_{\rm max}=0.5\,h/{\rm Mpc}\). We construct 20,000 simulated galaxy samples using our forward model, which is based on high-resolution \({\rm Q{\scriptsize UIJOTE}}\) \(N\)-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 \(\sim60\%\) larger than CMASS with standard methods. It results from additional cosmological information on non-linear scales beyond the limit of current analytic models, \(k > 0.25\,h/{\rm Mpc}\). While we focus on \(P_\ell\) in this work for validation and comparison to the literature, \({\rm S{\scriptsize IM}BIG}\) provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent \({\rm S{\scriptsize IM}BIG}\) analyses of summary statistics beyond \(P_\ell\).