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

Park, Ji Won; Birrer, Simon; Ueland, Madison; Cranmer, Miles; Agnello, Adriano; Wagner-Carena, Sebastian; Marshall, Philip J.; Roodman, Aaron

doi:10.3847/1538-4357/acdc25

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

Journal Article · Wed Aug 16 00:00:00 EDT 2023 · The Astrophysical Journal

DOI:https://doi.org/10.3847/1538-4357/acdc25· OSTI ID:1995626

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; Ueland, Madison;

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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 "external convergence" (κ_ext) from the lens environment and LOS is necessary for precise Hubble constant (H₀) inference. Starting from a large-scale simulation with a κ resolution of ~1', we introduce fluctuations on galaxy–galaxy lensing scales of ~1'' and extract random sight lines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1000 sight lines, the BGNN infers the individual κ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of κ precisely and without bias (within the 2σ credible interval), resulting in a contribution to the H₀ error budget well under 1%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sight line, extracts a stronger κ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the κ_ext characterization for precision TDC.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Contributing Organization:: LSST Dark Energy Science Collaboration

Grant/Contract Number:: AC02-76SF00515; AC02-05CH11231

OSTI ID:: 1995626

Alternate ID(s):: OSTI ID: 1999258; OSTI ID: 1999350

Journal Information:: The Astrophysical Journal, Journal Name: The Astrophysical Journal Vol. 953 Journal Issue: 2; ISSN 0004-637X

Publisher:: American Astronomical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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