{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H6FENZKQH536MDTAO6CV7WMQ6W","short_pith_number":"pith:H6FENZKQ","schema_version":"1.0","canonical_sha256":"3f8a46e5503f77e60e6077855fd990f583b9bed60ce5b01e77d8b2d238ffe277","source":{"kind":"arxiv","id":"2411.12629","version":1},"attestation_state":"computed","paper":{"title":"Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.IM","cs.AI"],"primary_cat":"astro-ph.GA","authors_text":"Annalisa Pillepich, Dylan Nelson, John F. Wu, Nikhil Garuda","submitted_at":"2024-11-19T16:40:17Z","abstract_excerpt":"Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\\rm{M}_{\\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\\rm{M}_{\\rm{halo}}$ from stellar mass ($\\rm{M}_{*}$) in simulated galaxy clusters using data from the IllustrisTNG simulation suite. Unlike traditional machine learning models like random forests, our GNN captures the information-rich substructure of galaxy clusters by using spatial and kinematic relationships between galaxy neighbour. A GNN model trained on the TNG-Cluster data"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.12629","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.GA","submitted_at":"2024-11-19T16:40:17Z","cross_cats_sorted":["astro-ph.CO","astro-ph.IM","cs.AI"],"title_canon_sha256":"fa3171e20d9190a4b5e8afed138fa1777c750606a1cdc0115f163a8b166b05ee","abstract_canon_sha256":"ab006b68e43dfbe7077546d76134370da664606b427efac55e252ef54ea0de48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:45.858025Z","signature_b64":"ev8vsdXa7R4+kb/36UAwJXhywzRX/nXORyPl3vkI0ZsoU/SG2JwsNJaVnos8UFmsVcE/+mgWMBS96hzNOXBzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f8a46e5503f77e60e6077855fd990f583b9bed60ce5b01e77d8b2d238ffe277","last_reissued_at":"2026-07-05T09:37:45.857578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:45.857578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.IM","cs.AI"],"primary_cat":"astro-ph.GA","authors_text":"Annalisa Pillepich, Dylan Nelson, John F. Wu, Nikhil Garuda","submitted_at":"2024-11-19T16:40:17Z","abstract_excerpt":"Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\\rm{M}_{\\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\\rm{M}_{\\rm{halo}}$ from stellar mass ($\\rm{M}_{*}$) in simulated galaxy clusters using data from the IllustrisTNG simulation suite. Unlike traditional machine learning models like random forests, our GNN captures the information-rich substructure of galaxy clusters by using spatial and kinematic relationships between galaxy neighbour. A GNN model trained on the TNG-Cluster data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12629","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.12629/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.12629","created_at":"2026-07-05T09:37:45.857633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.12629v1","created_at":"2026-07-05T09:37:45.857633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12629","created_at":"2026-07-05T09:37:45.857633+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6FENZKQH536","created_at":"2026-07-05T09:37:45.857633+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6FENZKQH536MDTA","created_at":"2026-07-05T09:37:45.857633+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6FENZKQ","created_at":"2026-07-05T09:37:45.857633+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12938","citing_title":"Cluster Mass Inference from Galaxy Kinematics","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W","json":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W.json","graph_json":"https://pith.science/api/pith-number/H6FENZKQH536MDTAO6CV7WMQ6W/graph.json","events_json":"https://pith.science/api/pith-number/H6FENZKQH536MDTAO6CV7WMQ6W/events.json","paper":"https://pith.science/paper/H6FENZKQ"},"agent_actions":{"view_html":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W","download_json":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W.json","view_paper":"https://pith.science/paper/H6FENZKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.12629&json=true","fetch_graph":"https://pith.science/api/pith-number/H6FENZKQH536MDTAO6CV7WMQ6W/graph.json","fetch_events":"https://pith.science/api/pith-number/H6FENZKQH536MDTAO6CV7WMQ6W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W/action/storage_attestation","attest_author":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W/action/author_attestation","sign_citation":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W/action/citation_signature","submit_replication":"https://pith.science/pith/H6FENZKQH536MDTAO6CV7WMQ6W/action/replication_record"}},"created_at":"2026-07-05T09:37:45.857633+00:00","updated_at":"2026-07-05T09:37:45.857633+00:00"}