Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ( κ ) from photometric measurements of galaxies along a given line of sight (LOS). The method is of particular interest in strong gravitational time-delay cosmography (TDC), where characterizing the “ext...

詳細記述

書誌詳細

出版年:	The Astrophysical Journal
主要な著者:	Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration
フォーマット:	論文
言語:	英語
出版事項:	IOP Publishing 2023-01-01
主題:	Cosmology Hubble constant Hierarchical models Bayesian statistics Neural networks Weak gravitational lensing
オンライン･アクセス:	https://doi.org/10.3847/1538-4357/acdc25