BibTeX

@article{2310.15256v1,
Author        = {Pablo Lemos and Liam Parker and ChangHoon Hahn and Shirley Ho and Michael Eickenberg and Jiamin Hou and Elena Massara and Chirag Modi and Azadeh Moradinezhad Dizgah and Bruno Regaldo-Saint Blancard and David Spergel},
Title         = {SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering},
Eprint        = {2310.15256v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power spectrum, $P_\ell$, with analytic models based on perturbation theory. Consequently, they do not fully exploit the non-linear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the {\sc SimBIG} forward modelling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with stochastic weight averaging to perform massive data compression of the galaxy field. We infer constraints on $Ω_m = 0.267^{+0.033}_{-0.029}$ and $σ_8=0.762^{+0.036}_{-0.035}$. While our constraints on $Ω_m$ are in-line with standard $P_\ell$ analyses, those on $σ_8$ are $2.65\times$ tighter. Our analysis also provides constraints on the Hubble constant $H_0=64.5 \pm 3.8 \ {\rm km / s / Mpc}$ from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with $P_\ell$. We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid.},
Year          = {2023},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2310.15256v1},
File          = {2310.15256v1.pdf}
}