# Cosmology Inference from Biased Tracers using the EFT-based Likelihood

## TL;DR

This paper applies an EFT-based likelihood approach to galaxy clustering data to infer cosmological parameters, demonstrating accurate recovery of the power spectrum normalization $\sigma_8$ with potential for improved robustness against small-scale complexities.

## Contribution

It introduces a novel EFT-based likelihood method that utilizes high-order information for cosmological inference from biased tracers, improving robustness and accuracy.

## Key findings

- Successfully recovered $\sigma_8$ within 95% CL across various samples and redshifts.
- Incorporated bias terms up to second order and leading higher-derivative terms.
- Identified potential sources of systematic bias for future refinement.

## Abstract

The effective-field-theory (EFT) approach to the clustering of galaxies and other biased tracers allows for an isolation of the cosmological information that is protected by symmetries, in particular the equivalence principle, and thus is robust to the complicated dynamics of dark matter, gas, and stars on small scales. All existing implementations proceed by making predictions for the lowest-order $n$-point functions of biased tracers, as well as their covariance, and comparing with measurements. Recently, we presented an EFT-based expression for the conditional probability of the density field of a biased tracer given the matter density field, which in principle combines information from arbitrarily high order $n$-point functions. Here, we report results based on this likelihood by applying it to halo catalogs in real space, specifically on the inference of the power spectrum normalization $\sigma_8$. We include bias terms up to second order as well as the leading higher-derivative term. For a cutoff value of $\Lambda = 0.1 h\,{\rm Mpc}^{-1}$, we recover the ground-truth value of $\sigma_8$ to within 95% CL for different halo samples and redshifts. We discuss possible sources for the remaining systematic bias in $\sigma_8$ as well as future developments.