Kavli Affiliate: Risa H. Wechsler
| First 5 Authors: Mahlet Shiferaw, Nickolas Kokron, Risa H. Wechsler, ,
| Summary:
Our ability to extract cosmological information from galaxy surveys is
limited by uncertainties in the galaxy-dark matter halo relationship for a
given galaxy population, which are governed by the intricacies of galaxy
formation. To quantify these uncertainties, we examine quenched and
star-forming galaxies using two distinct approaches to modeling galaxy
formation: UniverseMachine, an empirical semi-analytic model, and the
IllustrisTNG hydrodynamical simulation. We apply a second-order hybrid N-body
perturbative bias expansion to each galaxy sample, enabling direct comparison
of modeling approaches and revealing how uncertainties in galaxy formation and
the galaxy-halo connection affect bias parameters and non-Poisson noise across
number density and redshift. Notably, we find that quenched and star-forming
galaxies occupy distinct parts of bias parameter spacce, and that the scatter
induced from these entirely different galaxy formation models is small when
conditioned on similar selections of galaxies. We also detect a signature of
assembly bias in our samples; this leads to small but significant deviations
from predictions of the analytic bias, while samples with assembly bias removed
match these predictions well. This work indicates that galaxy samples from a
spectrum of reasonable, physically motivated models for galaxy formation
roughly spanning our current understanding give a relatively small range of
field-level galaxy bias parameters and relations. We estimate a set of priors
from this set of models that should be useful in extracting cosmological
information from LRG- and ELG-like samples. Looking forward, this indicates
that careful estimates of the range of impacts of galaxy formation for a given
sample and cosmological analysis will be an essential ingredient for extracting
the most precise cosmological information from current and future large galaxy
surveys.
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