{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2FSMVP7SHFGDPFSYAR7CCJKWQS","short_pith_number":"pith:2FSMVP7S","schema_version":"1.0","canonical_sha256":"d164cabff2394c379658047e2125568492b996c34442f86dac5913dded9beb01","source":{"kind":"arxiv","id":"2005.03546","version":1},"attestation_state":"computed","paper":{"title":"Efficient Fermi Source Identification with Machine Learning Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"Denise Costantin, Gaoyong Luo, Haitao Cao, Hubing Xiao, Junhui Fan, Zhiyuan Pei","submitted_at":"2020-05-07T15:13:32Z","abstract_excerpt":"In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the unassociated sources; 2) to identify BCUs into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs). Two dimensional reduction methods are presented to decrease computational complexity, where Random Forest (RF), Multilayer Perceptron (MLP) and Generative Adversarial Nets (GAN) ar"},"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":"2005.03546","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.HE","submitted_at":"2020-05-07T15:13:32Z","cross_cats_sorted":[],"title_canon_sha256":"b2bbe4f95cb9cd4ad79ae73da6d1cda447b01136eb94c9203a3e710da9f5f09d","abstract_canon_sha256":"d843699f77119d8795221bcd7cdb6fd7f25616d966d59b976ac10b3ec9c3df70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:01:09.984805Z","signature_b64":"2eOuhQGjF6wRlkqZhnmKaHA0ADGHwGR3A3HCVPCh1EYfu2sC/9k1IBuxj0D+N5709jCooJlMuZLkjD5hUyxJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d164cabff2394c379658047e2125568492b996c34442f86dac5913dded9beb01","last_reissued_at":"2026-07-05T01:01:09.984404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:01:09.984404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Fermi Source Identification with Machine Learning Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"Denise Costantin, Gaoyong Luo, Haitao Cao, Hubing Xiao, Junhui Fan, Zhiyuan Pei","submitted_at":"2020-05-07T15:13:32Z","abstract_excerpt":"In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the unassociated sources; 2) to identify BCUs into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs). Two dimensional reduction methods are presented to decrease computational complexity, where Random Forest (RF), Multilayer Perceptron (MLP) and Generative Adversarial Nets (GAN) ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.03546","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/2005.03546/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":"2005.03546","created_at":"2026-07-05T01:01:09.984465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.03546v1","created_at":"2026-07-05T01:01:09.984465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.03546","created_at":"2026-07-05T01:01:09.984465+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FSMVP7SHFGD","created_at":"2026-07-05T01:01:09.984465+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FSMVP7SHFGDPFSY","created_at":"2026-07-05T01:01:09.984465+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FSMVP7S","created_at":"2026-07-05T01:01:09.984465+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/2FSMVP7SHFGDPFSYAR7CCJKWQS","json":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS.json","graph_json":"https://pith.science/api/pith-number/2FSMVP7SHFGDPFSYAR7CCJKWQS/graph.json","events_json":"https://pith.science/api/pith-number/2FSMVP7SHFGDPFSYAR7CCJKWQS/events.json","paper":"https://pith.science/paper/2FSMVP7S"},"agent_actions":{"view_html":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS","download_json":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS.json","view_paper":"https://pith.science/paper/2FSMVP7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.03546&json=true","fetch_graph":"https://pith.science/api/pith-number/2FSMVP7SHFGDPFSYAR7CCJKWQS/graph.json","fetch_events":"https://pith.science/api/pith-number/2FSMVP7SHFGDPFSYAR7CCJKWQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS/action/storage_attestation","attest_author":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS/action/author_attestation","sign_citation":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS/action/citation_signature","submit_replication":"https://pith.science/pith/2FSMVP7SHFGDPFSYAR7CCJKWQS/action/replication_record"}},"created_at":"2026-07-05T01:01:09.984465+00:00","updated_at":"2026-07-05T01:01:09.984465+00:00"}