Perturbation theory models for LSST-era galaxy clustering: tests with sub-percent mock catalog measurements in Fourier and configuration space
Samuel Goldstein, Shivam Pandey, An\v{z}e Slosar, Jonathan Blazek,, Bhuvnesh Jain, The LSST Dark Energy Science Collaboration

TL;DR
This study demonstrates that a hybrid perturbation theory model can accurately describe non-linear galaxy bias at sub-percent precision levels in LSST-like galaxy clustering measurements, validated with high-fidelity simulations.
Contribution
The paper introduces a hybrid-perturbation theory model that effectively models non-linear galaxy bias at small scales with high precision, validated against mock LSST-era galaxy data.
Findings
Achieved 0.5% modeling precision for galaxy bias up to k=0.5 h/Mpc.
Linear bias parameter measured with 0.01% accuracy.
Including higher-order bias parameters improves small-scale modeling.
Abstract
We analyze the clustering of galaxies using the z=1.006 snapshot of the CosmoDC2 simulation, a high-fidelity synthetic galaxy catalog designed to validate analysis methods for the Large Synoptic Survey Telescope (LSST). We present sub-percent measurements of the galaxy auto-correlation and galaxy-dark matter cross correlations in Fourier space and configuration space for a magnitude-limited galaxy sample. At these levels of precision, the statistical errors of the measurement are comparable to the systematic effects present in the simulation and measurement procedure; nevertheless, using a hybrid-PT model, we are able to model non-linear galaxy bias with 0.5% precision up to scales of and . While the linear bias parameter is measured with 0.01% precision, other bias parameters are determined with considerably weaker…
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