{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UKAMCWEYI2QYNDDCIV6M6HQYET","short_pith_number":"pith:UKAMCWEY","schema_version":"1.0","canonical_sha256":"a280c1589846a1868c62457ccf1e1824c870d4aa99852a2e7161651d5c131346","source":{"kind":"arxiv","id":"1705.09832","version":1},"attestation_state":"computed","paper":{"title":"3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"David J. Thompson, David Salvetti, Giovanni La Mura, Graziano Chiaro","submitted_at":"2017-05-27T15:44:50Z","abstract_excerpt":"In its first four years of operation, the Fermi Large Area Telescope (LAT) detected 3033 $\\gamma$-ray emitting sources. In the Fermi-LAT Third Source Catalogue (3FGL) about 50% of the sources have no clear association with a likely $\\gamma$-ray emitter. We use an artificial neural network algorithm aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of 559 3FGL unassociated sources characterised by $\\gamma$-ray properties very similar to those of Active Galactic Nuclei. Based on our method, we can classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leav"},"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":"1705.09832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.HE","submitted_at":"2017-05-27T15:44:50Z","cross_cats_sorted":[],"title_canon_sha256":"a55846b906486e0503e916eff441c36bf71d6942c0b98bfcd261dfbae35783eb","abstract_canon_sha256":"a0c6b7842c99135e60d8f5958a09e895c0a234457fc2a0009f18998578861cd9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:03.320274Z","signature_b64":"jOMUs2jeiXI9idsBkeexffjbf4uahjF15gLdXRukzL/aJ+D2Tbc1fvLVkv1ixl3h35b0siEzHkzbghzZlDNbAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a280c1589846a1868c62457ccf1e1824c870d4aa99852a2e7161651d5c131346","last_reissued_at":"2026-05-18T00:40:03.319835Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:03.319835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"David J. Thompson, David Salvetti, Giovanni La Mura, Graziano Chiaro","submitted_at":"2017-05-27T15:44:50Z","abstract_excerpt":"In its first four years of operation, the Fermi Large Area Telescope (LAT) detected 3033 $\\gamma$-ray emitting sources. In the Fermi-LAT Third Source Catalogue (3FGL) about 50% of the sources have no clear association with a likely $\\gamma$-ray emitter. We use an artificial neural network algorithm aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of 559 3FGL unassociated sources characterised by $\\gamma$-ray properties very similar to those of Active Galactic Nuclei. Based on our method, we can classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leav"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09832","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":""},"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":"1705.09832","created_at":"2026-05-18T00:40:03.319905+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.09832v1","created_at":"2026-05-18T00:40:03.319905+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09832","created_at":"2026-05-18T00:40:03.319905+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKAMCWEYI2QY","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKAMCWEYI2QYNDDC","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKAMCWEY","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.11896","citing_title":"Unidentified Gamma-ray Sources as Targets for Indirect Dark Matter Detection with the Fermi-Large Area Telescope","ref_index":79,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET","json":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET.json","graph_json":"https://pith.science/api/pith-number/UKAMCWEYI2QYNDDCIV6M6HQYET/graph.json","events_json":"https://pith.science/api/pith-number/UKAMCWEYI2QYNDDCIV6M6HQYET/events.json","paper":"https://pith.science/paper/UKAMCWEY"},"agent_actions":{"view_html":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET","download_json":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET.json","view_paper":"https://pith.science/paper/UKAMCWEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.09832&json=true","fetch_graph":"https://pith.science/api/pith-number/UKAMCWEYI2QYNDDCIV6M6HQYET/graph.json","fetch_events":"https://pith.science/api/pith-number/UKAMCWEYI2QYNDDCIV6M6HQYET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET/action/storage_attestation","attest_author":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET/action/author_attestation","sign_citation":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET/action/citation_signature","submit_replication":"https://pith.science/pith/UKAMCWEYI2QYNDDCIV6M6HQYET/action/replication_record"}},"created_at":"2026-05-18T00:40:03.319905+00:00","updated_at":"2026-05-18T00:40:03.319905+00:00"}