{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:Y75FGQHCBKP7METLKGS5XSDAWR","short_pith_number":"pith:Y75FGQHC","schema_version":"1.0","canonical_sha256":"c7fa5340e20a9ff6126b51a5dbc860b47f0f6fbb5ff8d90e544842c48f5b51ac","source":{"kind":"arxiv","id":"2012.05251","version":2},"attestation_state":"computed","paper":{"title":"Classification of Fermi-LAT sources with deep learning using energy and time spectra","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.HE","authors_text":"Michael Kr\\\"amer, Silvia Manconi, Thorben Finke","submitted_at":"2020-12-09T19:00:04Z","abstract_excerpt":"Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or unassociated gamma-ray sources in the last release of the Fermi-LAT catalogue (4FGL-DR2) obtained with 10 years of data. In contrast to previous work, our method directly uses the measurements of the photon energy spectrum and time series as input for the classification, instead of specific, human-crafted features. Dense neural networks, and for the first time in th"},"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":"2012.05251","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.HE","submitted_at":"2020-12-09T19:00:04Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"ffde7965ffb1a43cb4582699cf572c4d462714b12f2cad810a08854d9dd3387c","abstract_canon_sha256":"9491f15689b8b8b0a8443dd7e1517b6eb0ece9a7c43e559abb72a1bfeef3fd0f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:29.695751Z","signature_b64":"tt7EaZEBcWdS6V7eNg74VLA22wT2OxOiH6Nte7qACE2qgoyCJg4tyfzKUlubKCrmqWF/ZxDXXWJU8QuvfiBxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7fa5340e20a9ff6126b51a5dbc860b47f0f6fbb5ff8d90e544842c48f5b51ac","last_reissued_at":"2026-07-05T03:17:29.695216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:29.695216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classification of Fermi-LAT sources with deep learning using energy and time spectra","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.HE","authors_text":"Michael Kr\\\"amer, Silvia Manconi, Thorben Finke","submitted_at":"2020-12-09T19:00:04Z","abstract_excerpt":"Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or unassociated gamma-ray sources in the last release of the Fermi-LAT catalogue (4FGL-DR2) obtained with 10 years of data. In contrast to previous work, our method directly uses the measurements of the photon energy spectrum and time series as input for the classification, instead of specific, human-crafted features. Dense neural networks, and for the first time in th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.05251","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/2012.05251/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":"2012.05251","created_at":"2026-07-05T03:17:29.695270+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.05251v2","created_at":"2026-07-05T03:17:29.695270+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.05251","created_at":"2026-07-05T03:17:29.695270+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y75FGQHCBKP7","created_at":"2026-07-05T03:17:29.695270+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y75FGQHCBKP7METL","created_at":"2026-07-05T03:17:29.695270+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y75FGQHC","created_at":"2026-07-05T03:17:29.695270+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07158","citing_title":"Weakly supervised machine learning for model-agnostic searches of new phenomena in the $\\gamma$-ray sky","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR","json":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR.json","graph_json":"https://pith.science/api/pith-number/Y75FGQHCBKP7METLKGS5XSDAWR/graph.json","events_json":"https://pith.science/api/pith-number/Y75FGQHCBKP7METLKGS5XSDAWR/events.json","paper":"https://pith.science/paper/Y75FGQHC"},"agent_actions":{"view_html":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR","download_json":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR.json","view_paper":"https://pith.science/paper/Y75FGQHC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.05251&json=true","fetch_graph":"https://pith.science/api/pith-number/Y75FGQHCBKP7METLKGS5XSDAWR/graph.json","fetch_events":"https://pith.science/api/pith-number/Y75FGQHCBKP7METLKGS5XSDAWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR/action/storage_attestation","attest_author":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR/action/author_attestation","sign_citation":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR/action/citation_signature","submit_replication":"https://pith.science/pith/Y75FGQHCBKP7METLKGS5XSDAWR/action/replication_record"}},"created_at":"2026-07-05T03:17:29.695270+00:00","updated_at":"2026-07-05T03:17:29.695270+00:00"}