Reducing Model Error Using Optimised Galaxy Selection: Weak Lensing Cluster Mass Estimation

TL;DR

This paper introduces an optimized galaxy selection method using variational inference to significantly reduce bias in weak lensing cluster mass measurements, crucial for high-precision cosmology.

Contribution

It presents a novel combinatorial optimization approach for galaxy selection that reduces systematic bias in cluster mass estimation from weak lensing data.

Findings

Reduces bias in mass estimates by 60-70%.

Maintains up to 90% of source galaxies.

Applicable to joint galaxy selection and model inference.

Abstract

Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time are expected to revolutionise the field, by enabling the analysis of cluster samples of unprecedented size and quality. To reach this era of high-precision cluster cosmology, the mitigation of sources of systematic error is crucial. A particularly important challenge is bias in cluster mass measurements induced by inaccurate photometric redshift estimates of source galaxies. This work proposes a method to optimise the source sample selection in cluster weak lensing analyses drawn from wide-field survey lensing catalogs to reduce the bias on reconstructed cluster masses. We use a combinatorial optimisation scheme and methods from variational inference to select galaxies in latent space to produce a probabilistic galaxy source sample catalog for highly accurate cluster mass estimation. We show that our method reduces the critical surface mass density $\Sigma_{\rm crit}$ modelling bias on the 60-70% level, while maintaining up to 90% of galaxies. We highlight that our methodology has applications beyond cluster mass estimation as an approach to jointly combine galaxy selection and model inference under sources of systematics.