Field-level simulation-based inference of galaxy clustering with convolutional neural networks

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 ${P}_{\ensuremath{\ell}}$, with analytic models based on perturbation theory. Consequently, they do not fully exploit the nonlinear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the SimBIG forward modeling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the Baryon Oscillation Spectroscopic Survey 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 ${\mathrm{\ensuremath{\Omega}}}_{m}={0.267}_{\ensuremath{-}0.029}^{+0.033}$ and ${\ensuremath{\sigma}}_{8}={0.762}_{\ensuremath{-}0.035}^{+0.036}$. While our constraints on ${\mathrm{\ensuremath{\Omega}}}_{m}$ are in line with standard ${P}_{\ensuremath{\ell}}$ analyses, ours on ${\ensuremath{\sigma}}_{8}$ are $2.65\ifmmode\times\else\texttimes\fi{}$ tighter. Our analysis also provides constraints on the Hubble constant ${H}_{0}=64.5\ifmmode\pm\else\textpm\fi{}3.8\text{ }\text{ }\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$ from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with ${P}_{\ensuremath{\ell}}$. 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 the Dark Energy Spectroscopic Instrument, Prime Focus Spectrograph, and Euclid.

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