{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PPZ2UY5MKO2QFB4XVCD4I4SX3C","short_pith_number":"pith:PPZ2UY5M","schema_version":"1.0","canonical_sha256":"7bf3aa63ac53b5028797a887c47257d891de2e878ea0818e27431b6d6f37e3ac","source":{"kind":"arxiv","id":"2303.00005","version":2},"attestation_state":"computed","paper":{"title":"Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Arya Farahi, August Evrard, Daisuke Nagai, John Soltis, Matthew Ho, Michelle Ntampaka","submitted_at":"2023-02-28T19:00:00Z","abstract_excerpt":"We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish these constraints using a catalogue of realistic mock eROSITA X-ray observations which use hydrodynamical simulations to model realistic cluster morphology, background emission, telescope response, and AGN sources. Using bolometric X-ray photon maps as input, DL models achieve a predictive mass scatter of $\\sigma_{\\ln M_\\mathrm{500c}} = 17.8\\%$, a factor of two improvements on scalar observables such as richness $N_\\m"},"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":"2303.00005","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.CO","submitted_at":"2023-02-28T19:00:00Z","cross_cats_sorted":[],"title_canon_sha256":"bba8159723ae75f3a5686f2ef6aa6237c302bbaf626635e0ba19420186505ac2","abstract_canon_sha256":"ae9c4b71e371107da2fb5056c2f4c5067addb363c0d81c3121065c6cfe1db2fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:34:47.639070Z","signature_b64":"s+c0RFGB9qT6j0DC9tKWqBJuJ6+8temjGrODcbNOIPeGiyU/vT4BWUNi+L+nsYq1hxCyfaYBvBpRbwL2+CAHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bf3aa63ac53b5028797a887c47257d891de2e878ea0818e27431b6d6f37e3ac","last_reissued_at":"2026-07-05T06:34:47.638509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:34:47.638509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Arya Farahi, August Evrard, Daisuke Nagai, John Soltis, Matthew Ho, Michelle Ntampaka","submitted_at":"2023-02-28T19:00:00Z","abstract_excerpt":"We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish these constraints using a catalogue of realistic mock eROSITA X-ray observations which use hydrodynamical simulations to model realistic cluster morphology, background emission, telescope response, and AGN sources. Using bolometric X-ray photon maps as input, DL models achieve a predictive mass scatter of $\\sigma_{\\ln M_\\mathrm{500c}} = 17.8\\%$, a factor of two improvements on scalar observables such as richness $N_\\m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.00005","kind":"arxiv","version":2},"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/2303.00005/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":"2303.00005","created_at":"2026-07-05T06:34:47.638573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.00005v2","created_at":"2026-07-05T06:34:47.638573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.00005","created_at":"2026-07-05T06:34:47.638573+00:00"},{"alias_kind":"pith_short_12","alias_value":"PPZ2UY5MKO2Q","created_at":"2026-07-05T06:34:47.638573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PPZ2UY5MKO2QFB4X","created_at":"2026-07-05T06:34:47.638573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PPZ2UY5M","created_at":"2026-07-05T06:34:47.638573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C","json":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C.json","graph_json":"https://pith.science/api/pith-number/PPZ2UY5MKO2QFB4XVCD4I4SX3C/graph.json","events_json":"https://pith.science/api/pith-number/PPZ2UY5MKO2QFB4XVCD4I4SX3C/events.json","paper":"https://pith.science/paper/PPZ2UY5M"},"agent_actions":{"view_html":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C","download_json":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C.json","view_paper":"https://pith.science/paper/PPZ2UY5M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.00005&json=true","fetch_graph":"https://pith.science/api/pith-number/PPZ2UY5MKO2QFB4XVCD4I4SX3C/graph.json","fetch_events":"https://pith.science/api/pith-number/PPZ2UY5MKO2QFB4XVCD4I4SX3C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C/action/storage_attestation","attest_author":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C/action/author_attestation","sign_citation":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C/action/citation_signature","submit_replication":"https://pith.science/pith/PPZ2UY5MKO2QFB4XVCD4I4SX3C/action/replication_record"}},"created_at":"2026-07-05T06:34:47.638573+00:00","updated_at":"2026-07-05T06:34:47.638573+00:00"}