Mitigation of nonlinear galaxy bias with a theoretical-error likelihood

TL;DR

This paper introduces a method to mitigate theoretical uncertainties from nonlinear galaxy bias in small-scale galaxy survey analyses by constructing a covariance matrix for theoretical errors, improving the robustness and precision of cosmological parameter estimation.

Contribution

The authors develop a covariance-based approach to account for nonlinear galaxy bias uncertainties, offering an alternative to traditional scale cuts in galaxy survey analyses.

Findings

Most configurations match scale cut results in precision and accuracy.

In some cases, the method yields stronger constraints on cosmological parameters.

The approach is effective regardless of the maximum scale used in the analysis.

Abstract

Stage-IV galaxy surveys will measure correlations at small cosmological scales with high signal-to-noise ratio. One of the main challenges of extracting information from small scales is devising accurate models, as well as characterizing the theoretical uncertainties associated with any given model. In this work, we explore the mitigation of theoretical uncertainty due to nonlinear galaxy bias in the context of photometric 2$\times$2-pt analyses. We consider linear galaxy bias as the fiducial model and derive the contribution to the covariance matrix induced by neglected higher-order bias. We construct a covariance matrix for the theoretical error in galaxy clustering and galaxy-galaxy lensing using simulation-based relations that connect higher-order parameters to linear bias. We test the modified likelihood in 2$\times$2-pt analyses based on two sets of mock data vectors: (1) simulated data vectors, constructed from those same relations between bias parameters, and (2) data vectors based on the AbacusSummit simulation suite. We then compare the performance of the theoretical-error approach to the commonly employed scale cuts. We find most theoretical-error configurations yield results equivalent to the scale cuts in terms of precision and accuracy, in some cases producing significantly stronger bounds on cosmological parameters. These results are independent of the maximum scale $k_\mathrm{max}$ in the analysis with theoretical error. The scenarios where linear bias supplemented by theoretical error is unable to recover unbiased cosmology are connected to inadequate modeling of the $gg$-$g\kappa$ covariance of theoretical error. In view of its removing the ambiguity in the choice of $k_\mathrm{max}$, as well as the possibility of attaining higher precision than the usual scale cuts, we consider this method to be promising for analyses of LSS in upcoming photometric galaxy surveys.