On the Relation Between Field-Level Posteriors, Correlators, and their Likelihoods

TL;DR

This paper develops a comprehensive framework for analyzing cosmological data at the field level, connecting likelihoods, correlators, and information content, applicable to various models and data types.

Contribution

It introduces a non-perturbative, general method to relate field-level posteriors with correlators and likelihoods, enhancing understanding of information loss and optimal reconstruction.

Findings

Derives a general Fisher matrix expression from the field-level posterior.

Shows how likelihoods based on power spectrum and bispectrum relate to full field information.

Demonstrates optimal BAO scale reconstruction and initial condition estimation.

Abstract

We develop a field-level posterior for cosmological data by marginalizing over initial conditions and noise in a general forward model. While our focus is on large-scale structure data, the results generalize to any weakly non-Gaussian observable. Moreover, the construction is non-perturbative with respect to the forward model and applies equally well to perturbative calculations, simulation-based predictions, and more general effective descriptions. Expanding the FLP around its Gaussian limit, we derive a general expression for the Fisher matrix and reorganize the field-level information into contributions associated with the connected correlators of the evolved field. This makes explicit which terms are captured by likelihood analyses based on the power spectrum, the bispectrum, or finite sets of summary statistics, and which are lost under compression. We recover the standard Gaussian-covariance result for the power spectrum, show that the Gaussian bispectrum likelihood reproduces the corresponding field-level contribution, and show how cross-covariances among summaries progressively reconstruct more of the full field-level information. As an application to the BAO scale, we show how the field contains all the information required for its optimal reconstruction in the presence of noise, and identify the contributions in the FLP needed to attain this limit. We also show that the reconstruction of the initial field arises naturally as a byproduct of our approach, yielding the optimal estimate of the initial conditions given the data and the noise. Our results provide a unified framework to compare field-level and correlator-based inference, to quantify the information loss induced by compression, and to explore the role of stochasticity.