A Bayesian Hierarchical Approach to Galaxy-Galaxy Lensing
Alessandro Sonnenfeld (1), Alexie Leauthaud (1, 2) ((1) Kavli IPMU,, (2) UC Santa Cruz)

TL;DR
This paper introduces a Bayesian hierarchical method for galaxy-galaxy lensing analysis that improves the accuracy of scaling relation measurements by properly accounting for scatter and avoiding data stacking.
Contribution
The paper presents a novel Bayesian hierarchical inference formalism that enhances the analysis of weak lensing data, enabling unbiased constraints on galaxy-halo relations without stacking.
Findings
Accurately recovers mean halo mass and concentration with 0.1 dex precision.
Effectively constrains intrinsic scatter in halo mass with 0.05 dex accuracy.
Reduces bias caused by observational scatter in traditional stacking methods.
Abstract
We present a Bayesian hierarchical inference formalism to study the relation between the properties of dark matter halos and those of their central galaxies using weak gravitational lensing. Unlike traditional methods, this technique does not resort to stacking the weak lensing signal in bins, and thus allows for a more efficient use of the information content in the data. Our method is particularly useful for constraining scaling relations between two or more galaxy properties and dark matter halo mass, and can also be used to constrain the intrinsic scatter in these scaling relations. We show that, if observational scatter is not properly accounted for, the traditional stacking method can produce biased results when exploring correlations between multiple galaxy properties and halo mass. For example, this bias can affect studies of the joint correlation between galaxy mass, halo mass,…
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