Kavli Affiliate: Risa H. Wechsler
| First 5 Authors: , , , ,
| 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 semianalytic 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 the galaxy-halo
connection affect bias parameters and non-Poisson noise across number densities
and redshifts. Notably, we find that quenched and star-forming galaxies occupy
distinct parts of the bias parameter space, and that the scatter induced from
these different galaxy formation models is small when conditioned on similar
selections of galaxies. We also detect the signature of assembly bias in our
samples; this leads to small but significant deviations from analytic bias
predictions, while assembly bias-removed samples match these predictions well.
This work indicates that galaxy samples from a spectrum of reasonable,
physically motivated models for galaxy formation give a relatively small range
of field-level galaxy bias parameters. We estimate a set of priors from these
models that should be useful in extracting cosmological constraints from
luminous red galaxy- and emission line galaxy-like samples. Looking forward,
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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