Field-level vs summaries: convergence of information in non-Gaussian density fields

Abstract

We elucidate the sources of information gain in weakly non-Gaussian cosmological fields at the field- vs. summary-statistic-level in a controlled setting. Specifically, we compare field-level inference (FLI) with the standard power spectrum plus bispectrum (P${+}$B), and a family of composite-operator correlators (OCs) built from auto- and cross-spectra of local powers of the galaxy density field. The forward model is a linear density field with a single local quadratic coupling $\lambda$ and Gaussian noise; this minimal nonlinear setup interpolates between a purely Gaussian dataset ($\lambda=0$) and a non-Gaussian one ($\lambda\sim 1$), while keeping the analytical structure tractable. FLI is performed by jointly sampling the initial conditions, bias and noise parameters via MCMC; the summary posteriors are obtained with simulation-based inference (SBI) as well as Fisher estimates. In the Gaussian limit, the P${+}$B, OCs and FLI yield equivalent constraints, in agreement with the perturbative expectation. As the nonlinear coupling $\lambda$ increases, the summary-based uncertainties on the model parameters grow faster than the FLI ones, leading to an increasing information loss for a fixed set of summaries. This loss is largely, but not completely, recovered by adding OCs corresponding to up to the 6-point function. The information loss over FLI becomes even more pronounced for lower-noise data, where summaries corresponding to up to the 6-point function still capture significantly less information than the field.