How Much Information Can Be Extracted from Galaxy Clustering at the Field Level?
Nhat-Minh Nguyen, Fabian Schmidt, Beatriz Tucci, Martin Reinecke, Andrija Kostić
Abstract
We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of cosmic large-scale structure, specifically the amplitude ${\ensuremath{\sigma}}_{8}$ of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of $8\text{ }\text{ }({h}^{\ensuremath{-}1}\text{ }\mathrm{Gpc}{)}^{3}$. Our constraint on ${\ensuremath{\sigma}}_{8}$ is entirely due to nonlinear information, and obtained by explicitly sampling the initial conditions along with tracer bias and noise parameters via a Lagrangian effective field theory-based forward model, leftfield. The comparison with a simulation-based inference of the power spectrum and bispectrum---likewise using the leftfield forward model---shows that, when including precisely the same modes of the same data up to ${k}_{\mathrm{max}}=0.10\text{ }\text{ }h\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$ ($0.12\text{ }\text{ }h\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$), the field-level approach yields a factor of 3.5 (5.2) improvement in the ${\ensuremath{\sigma}}_{8}$ constraint, going from 20.0% to 5.7% (17.0% to 3.3%). This study provides direct insights into cosmological information encoded in galaxy clustering beyond low-order $n$-point functions.