DESI Mock Challenge: Halo and galaxy catalogs with the bias assignment method
Andr\'es Balaguera-Antol\'inez, Francisco-Shu Kitaura, Shadab Alam,, Chia-Hsun Chuang, Yu Yu, Ginevra Favole, Cheng Zhao, Francesco Sinigaglia,, David Brooks, Axel de la Macorra, Andreu Font-Ribera, Satya Gontcho A, Gontcho, Klaus Honscheid, Robert Kehoe, Aron Meisner

TL;DR
This paper introduces BAM, a bias assignment method that efficiently creates accurate mock galaxy catalogs with reliable covariance matrices for large-scale structure analysis, crucial for upcoming galaxy surveys like DESI.
Contribution
The paper presents a novel bias assignment approach (BAM) that models halo and galaxy distributions with high accuracy using minimal simulations, improving mock catalog generation for cosmological studies.
Findings
BAM achieves 3-10% accuracy in power spectrum multipoles up to k~0.4 h/Mpc.
Mock catalogs reproduce two- and three-point statistics with percent-level precision.
Covariance matrices from BAM resemble those from detailed N-body simulations.
Abstract
We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed -body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. Using specific models of halo occupation distributions, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the…
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