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...

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Autori principali:	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
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IOP Publishing 2023-01-01
Serie:	The Astrophysical Journal
Soggetti:	Cosmology Hubble constant Hierarchical models Bayesian statistics Neural networks Weak gravitational lensing
Accesso online:	https://doi.org/10.3847/1538-4357/acdc25
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