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research article

Very massive tracers and higher derivative biases

Fujita, Tomohiro
•
Mauerhofer, Valentin  
•
Senatore, Leonardo
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January 1, 2020
Journal Of Cosmology And Astroparticle Physics

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, highly massive 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: at z = 0, for all tracers, we are able to match the power spectra up to k similar or equal to 0.28h Mpc(-1), as well as a small set of about 10(2) bispectra triangles up to k similar or equal to 0.17h Mpc. We also discuss the limitations of our study and some avenues to pursue to further establish these findings.

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Type
research article
DOI
10.1088/1475-7516/2020/01/009
Web of Science ID

WOS:000528025800010

Author(s)
Fujita, Tomohiro
Mauerhofer, Valentin  
Senatore, Leonardo
Vlah, Zvonimir
Angulo, Raul
Date Issued

2020-01-01

Published in
Journal Of Cosmology And Astroparticle Physics
Issue

1

Start page

009

Subjects

Astronomy & Astrophysics

•

Physics, Particles & Fields

•

Physics

•

cosmological perturbation theory

•

cosmological parameters from lss

•

power spectrum

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LPPC  
Available on Infoscience
May 8, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/168649
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