The Effective Field Theory of Large-Scale Structure and Multi-tracer

TL;DR

This paper evaluates how combining the perturbative bias expansion with the multi-tracer technique improves the accuracy and precision of cosmological parameter estimation from large-scale structure data.

Contribution

It demonstrates that multi-tracer analysis reduces biases and enhances constraints on cosmological parameters and bias models compared to single-tracer methods.

Findings

Multi-tracer reduces bias in $\\omega_{\rm cdm}$ and $h$ estimates.

Multi-tracer improves parameter constraints by about 60%.

Including tracer correlation still yields substantial gains.

Abstract

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in $\omega_{\rm cdm}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers. We also include a study with galaxies showing that multi-tracer still lead to substantial gains in the cosmological parameters.