Very Massive Tracers and Higher Derivative Biases

TL;DR

This paper demonstrates that including higher derivative biases in the Effective Field Theory of Large Scale Structures allows for accurate modeling of biased tracers' clustering, matching simulations up to certain scales.

Contribution

It shows that higher derivative biases are essential for modeling highly biased tracers accurately within EFTofLSS, improving predictions for their clustering statistics.

Findings

Good agreement with simulations for bispectra up to k≈0.17 h/Mpc at z=0.

Power spectra match simulations at higher wavenumbers.

Higher derivative biases improve modeling of biased tracers.

Abstract

Most of the upcoming cosmological information will come from analyzing the clustering of the Large Scale Structures (LSS) of the universe through LSS or CMB observations. It is therefore essential to be able to understand their behavior with exquisite precision. The Effective Field Theory of Large Scale Structures (EFTofLSS) provides a consistent framework to make predictions for LSS observables in the mildly non-linear regime. In this paper we focus on biased tracers. We argue that in calculations at a given order in the dark matter perturbations, highly biased tracers will underperform because of their larger higher derivative biases. A natural prediction of the EFTofLSS is therefore that by simply adding higher derivative biases, all tracers should perform comparably well. We implement this prediction for the halo-halo and the halo-matter power spectra at one loop, and the halo-halo-halo, halo-halo-matter, and halo-matter-matter bispectra at tree-level, and compare with simulations. We find good agreement with the prediction: for all tracers, we are able to match the bispectra up to $k\simeq0.17\,h/$Mpc at $z=0$ and the power spectra to a higher wavenumber.