{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hierarchical-inference-of-the-lensing","title":"Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks","arxiv_id":"2211.07807","date":"2022-11-15","proceeding":null,"authors":["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"],"abstract":"We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ($\\kappa$) from photometric measurements of galaxies along a given line of sight. The method is of particular interest in strong gravitational time delay cosmography (TDC), where characterizing the \"external convergence\" ($\\kappa_{\\rm ext}$) from the lens environment and line of sight is necessary for precise inference of the Hubble constant ($H_0$). Starting from a large-scale simulation with a $\\kappa$ resolution of $\\sim$1$'$, we introduce fluctuations on galaxy-galaxy lensing scales of $\\sim$1$''$ and extract random sightlines 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 1,000 sightlines, the BGNN infers the individual $\\kappa$ 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 $\\kappa$ precisely and without bias, resulting in a contribution to the $H_0$ 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 sightline, extracts more $\\kappa$ 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 $\\kappa_{\\rm ext}$ characterization for precision TDC. The implementation of our pipeline is available as a public Python package, Node to Joy.","url_abs":"https://arxiv.org/abs/2211.07807v1","url_pdf":"https://arxiv.org/pdf/2211.07807v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hierarchical-inference-of-the-lensing","repo_url":"https://github.com/jiwoncpark/node-to-joy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.07807","atlas_url":"https://app.syntology.ai/?focus=2211.07807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07807"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiwoncpark/node-to-joy","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"45d40eb704570c03","entry":"get_distance","repo":"jiwoncpark/node-to-joy","repo_kind":"official","path":"n2j/trainval_data/utils/coord_utils.py","file_url":"https://github.com/jiwoncpark/node-to-joy/blob/HEAD/n2j/trainval_data/utils/coord_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"45d40eb704570c03"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}