SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
Pablo Lemos Liam Parker ChangHoon Hahn Shirley Ho Michael Eickenberg Jiamin Hou Elena Massara Chirag Modi Azadeh Moradinezhad Dizgah Bruno Regaldo-Saint Blancard David Spergel
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 $\Omega_m
= 0.267^{+0.033}_{-0.029}$ and $\sigma_8=0.762^{+0.036}_{-0.035}$. While our
constraints on $\Omega_m$ are in-line with standard $P_\ell$ analyses, those on
$\sigma_8$ 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.