${\rm S{\scriptsize IM}BIG}$: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering

TL;DR

${\rm SIMBIG}$ is a forward modeling framework that uses simulation-based inference to analyze galaxy clustering, accurately inferring cosmological parameters while accounting for observational systematics and validated through a comprehensive mock challenge.

Contribution

The paper introduces ${\rm SIMBIG}$, a novel forward modeling approach for galaxy clustering analysis that incorporates realistic survey effects and demonstrates unbiased parameter inference through extensive validation.

Findings

Constraints on $\Omega_m$ and $\sigma_8$ are unbiased and conservative.

${\rm SIMBIG}$ effectively models observational systematics.

Framework is robust for non-linear scale cosmological inference.

Abstract

Simulation-Based Inference of Galaxies (${\rm S{\scriptsize IM}BIG}$) is a forward modeling framework for analyzing galaxy clustering using simulation-based inference. In this work, we present the ${\rm S{\scriptsize IM}BIG}$ forward model, which is designed to match the observed SDSS-III BOSS CMASS galaxy sample. The forward model is based on high-resolution ${\rm Q{\scriptsize UIJOTE}}$ $N$-body simulations and a flexible halo occupation model. It includes full survey realism and models observational systematics such as angular masking and fiber collisions. We present the "mock challenge" for validating the accuracy of posteriors inferred from ${\rm S{\scriptsize IM}BIG}$ using a suite of 1,500 test simulations constructed using forward models with a different $N$-body simulation, halo finder, and halo occupation prescription. As a demonstration of ${\rm S{\scriptsize IM}BIG}$, we analyze the power spectrum multipoles out to $k_{\rm max} = 0.5\,h/{\rm Mpc}$ and infer the posterior of $\Lambda$CDM cosmological and halo occupation parameters. Based on the mock challenge, we find that our constraints on $\Omega_m$ and $\sigma_8$ are unbiased, but conservative. Hence, the mock challenge demonstrates that ${\rm S{\scriptsize IM}BIG}$ provides a robust framework for inferring cosmological parameters from galaxy clustering on non-linear scales and a complete framework for handling observational systematics. In subsequent work, we will use ${\rm S{\scriptsize IM}BIG}$ to analyze summary statistics beyond the power spectrum including the bispectrum, marked power spectrum, skew spectrum, wavelet statistics, and field-level statistics.