A 1% accurate method to include baryonic effects in galaxy-galaxy lensing models

TL;DR

This paper introduces a baryonic correction model for galaxy-galaxy lensing that achieves 1% accuracy, enabling unbiased cosmological inference on small scales in large-scale structure surveys.

Contribution

The authors develop and validate a baryonic correction method for the galaxy-matter cross-power spectrum, improving modeling accuracy for galaxy-galaxy lensing analyses.

Findings

Ignoring baryons biases galaxy bias estimates.

Including baryonic effects removes biases in parameter inference.

Model achieves 1% accuracy across simulated variations.

Abstract

Galaxy clustering and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys. Unfortunately, the complicated relationship between galaxies and matter limits the exploitation of this data. Galaxy bias models -- such as the hybrid Lagrangian bias expansion -- allow describing galaxy clustering down to scales as small as $k = 0.7h$/Mpc. However, the galaxy-matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy-galaxy lensing. We propose to extend models of the galaxy-matter cross-power spectrum $P_{\rm gm}(k)$ (currently only accounting for dark matter) by including a baryonic correction inferred from the matter component ($S_{\rm mm}(k)$), so that $P_{\rm gm, full \, physics} (k) = \sqrt{S_{\rm mm}} P_{\rm gm, gravity \, only}$. We use the FLAMINGO simulations to measure the effect of baryons on the galaxy-matter cross-power spectrum and to assess the performance of our model. We perform a Bayesian analysis of synthetic data, implementing a model based on BACCO's hybrid Lagrangian bias expansion (for the nonlinear galaxy bias) and Baryon Correction Model. Ignoring baryons in the galaxy-matter cross-power spectrum leads to a biased inference of the galaxy bias, while ignoring baryons in both the galaxy-matter and matter-matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1% accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models ($k < 0.7h$/Mpc). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1$\sigma$ with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.