A rigorous EFT-based forward model for large-scale structure

TL;DR

This paper introduces a new Bayesian likelihood approach for large-scale structure analysis in cosmology, based on effective field theory, enabling unbiased inference of parameters like  from nonlinear data.

Contribution

It develops a perturbatively controlled likelihood framework using EFT that captures key information from galaxy density fields, improving cosmological parameter estimation.

Findings

Unbiased  estimates from simulated data.

Likelihood captures information equivalent to power spectrum, bispectrum, and BAO.

Demonstrates perturbative control in nonlinear regime.

Abstract

Conventional approaches to cosmology inference from galaxy redshift surveys are based on n-point functions, which are under rigorous perturbative control on sufficiently large scales. Here, we present an alternative approach, which employs a likelihood at the level of the galaxy density field. By integrating out small-scale modes based on effective-field theory arguments, we prove that this likelihood is under perturbative control if certain specific conditions are met. We further show that the information captured by this likelihood is equivalent to the combination of the next-to-leading order galaxy power spectrum, leading-order bispectrum, and BAO reconstruction. Combined with MCMC sampling and MAP optimization techniques, our results allow for fully Bayesian cosmology inference from large-scale structure that is under perturbative control. We illustrate this via a first demonstration of unbiased cosmology inference from nonlinear large-scale structure using this likelihood. In particular, we show unbiased estimates of the power spectrum normalization $\sigma_{8}$ from a catalog of simulated dark matter halos, where nonlinear information is crucial in breaking the $b_{1} - \sigma_{8}$ degeneracy.