{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IW4K3AUDZXMLKH2OQOBNQXJH7K","short_pith_number":"pith:IW4K3AUD","schema_version":"1.0","canonical_sha256":"45b8ad8283cdd8b51f4e8382d85d27fa9362b8d0894d0b29184d98806d50645a","source":{"kind":"arxiv","id":"1908.02765","version":2},"attestation_state":"computed","paper":{"title":"Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Daisuke Nagai, Dominique Eckert, John A. ZuHone, Klaus Dolag, Lorenzo Lovisari, Michelle Ntampaka, Sheridan B. Green","submitted_at":"2019-08-07T18:00:02Z","abstract_excerpt":"We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble learning method based on decision tree regression, to predict cluster masses using an input feature set. The feature set uses core-excised X-ray luminosity and a variety of morphological parameters, including surface brightness concentration, smoothness, asymmetry, power ratios, and ellipticity. The regressor is cross-validated and calibrated on a training sa"},"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":"1908.02765","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-08-07T18:00:02Z","cross_cats_sorted":[],"title_canon_sha256":"ca87b55974ccca909ca760f03ac7dbaca3f4535ad5946cd30b4b893e4ddad7c5","abstract_canon_sha256":"76313aeee49824dd6295b02c10f735c4fe1d22182f956e61fa5513f227882b9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:11:21.921857Z","signature_b64":"Ah99viUDqU/HPtxXLJwbiPra6IyDpk4BN/WA65mq/2hhtCHo+W4Vh9/hlGtA/+T0x6T2kbh6ECtUs02Ojz2+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45b8ad8283cdd8b51f4e8382d85d27fa9362b8d0894d0b29184d98806d50645a","last_reissued_at":"2026-07-05T00:11:21.921391Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:11:21.921391Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Daisuke Nagai, Dominique Eckert, John A. ZuHone, Klaus Dolag, Lorenzo Lovisari, Michelle Ntampaka, Sheridan B. Green","submitted_at":"2019-08-07T18:00:02Z","abstract_excerpt":"We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble learning method based on decision tree regression, to predict cluster masses using an input feature set. The feature set uses core-excised X-ray luminosity and a variety of morphological parameters, including surface brightness concentration, smoothness, asymmetry, power ratios, and ellipticity. The regressor is cross-validated and calibrated on a training sa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.02765","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/1908.02765/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":"1908.02765","created_at":"2026-07-05T00:11:21.921448+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.02765v2","created_at":"2026-07-05T00:11:21.921448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.02765","created_at":"2026-07-05T00:11:21.921448+00:00"},{"alias_kind":"pith_short_12","alias_value":"IW4K3AUDZXML","created_at":"2026-07-05T00:11:21.921448+00:00"},{"alias_kind":"pith_short_16","alias_value":"IW4K3AUDZXMLKH2O","created_at":"2026-07-05T00:11:21.921448+00:00"},{"alias_kind":"pith_short_8","alias_value":"IW4K3AUD","created_at":"2026-07-05T00:11:21.921448+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/IW4K3AUDZXMLKH2OQOBNQXJH7K","json":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K.json","graph_json":"https://pith.science/api/pith-number/IW4K3AUDZXMLKH2OQOBNQXJH7K/graph.json","events_json":"https://pith.science/api/pith-number/IW4K3AUDZXMLKH2OQOBNQXJH7K/events.json","paper":"https://pith.science/paper/IW4K3AUD"},"agent_actions":{"view_html":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K","download_json":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K.json","view_paper":"https://pith.science/paper/IW4K3AUD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.02765&json=true","fetch_graph":"https://pith.science/api/pith-number/IW4K3AUDZXMLKH2OQOBNQXJH7K/graph.json","fetch_events":"https://pith.science/api/pith-number/IW4K3AUDZXMLKH2OQOBNQXJH7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K/action/storage_attestation","attest_author":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K/action/author_attestation","sign_citation":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K/action/citation_signature","submit_replication":"https://pith.science/pith/IW4K3AUDZXMLKH2OQOBNQXJH7K/action/replication_record"}},"created_at":"2026-07-05T00:11:21.921448+00:00","updated_at":"2026-07-05T00:11:21.921448+00:00"}