{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YROZ6QLZFXZIKTLTTMJAW7ZLFU","short_pith_number":"pith:YROZ6QLZ","schema_version":"1.0","canonical_sha256":"c45d9f41792df2854d739b120b7f2b2d173c4a96347e7479dbc8e7095540fb9b","source":{"kind":"arxiv","id":"2004.13877","version":2},"attestation_state":"computed","paper":{"title":"Classifying Image Sequences of Astronomical Transients with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"astro-ph.IM","authors_text":"Catalina G\\'omez, Jaime E. Forero-Romero, Marcela Hern\\'andez Hoyos, Mauricio Neira, Pablo Arbel\\'aez","submitted_at":"2020-04-28T22:29:01Z","abstract_excerpt":"Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the expert's knowledge to find heuristic features to process the images, for instance, by performing image subtraction or by extracting sparse information such as flux time series, also known as light curves. We present a successful deep learning approach that learns directly from imaging data. Our method models explicitly the spatio-temporal patterns with Deep Convo"},"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":"2004.13877","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2020-04-28T22:29:01Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7a1d801bb657f11e55b8eb32fc34523fed843615c7cd1297ce83cd80f3aa61e3","abstract_canon_sha256":"c45d947449fc810750601b03d3a73e243ccafc6ac78ece3c1c655cce638d2487"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:27.244629Z","signature_b64":"ja1FY/XF/TJqmcpUpI+9GSeUNHKFosqVw7/nx6OWPlCYhpMJ4nEz520ux9SyU9fnt4nt0d5YAQlIKbVHptdaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c45d9f41792df2854d739b120b7f2b2d173c4a96347e7479dbc8e7095540fb9b","last_reissued_at":"2026-07-05T01:40:27.244177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:27.244177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classifying Image Sequences of Astronomical Transients with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"astro-ph.IM","authors_text":"Catalina G\\'omez, Jaime E. Forero-Romero, Marcela Hern\\'andez Hoyos, Mauricio Neira, Pablo Arbel\\'aez","submitted_at":"2020-04-28T22:29:01Z","abstract_excerpt":"Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the expert's knowledge to find heuristic features to process the images, for instance, by performing image subtraction or by extracting sparse information such as flux time series, also known as light curves. We present a successful deep learning approach that learns directly from imaging data. Our method models explicitly the spatio-temporal patterns with Deep Convo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13877","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/2004.13877/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":"2004.13877","created_at":"2026-07-05T01:40:27.244235+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.13877v2","created_at":"2026-07-05T01:40:27.244235+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13877","created_at":"2026-07-05T01:40:27.244235+00:00"},{"alias_kind":"pith_short_12","alias_value":"YROZ6QLZFXZI","created_at":"2026-07-05T01:40:27.244235+00:00"},{"alias_kind":"pith_short_16","alias_value":"YROZ6QLZFXZIKTLT","created_at":"2026-07-05T01:40:27.244235+00:00"},{"alias_kind":"pith_short_8","alias_value":"YROZ6QLZ","created_at":"2026-07-05T01:40:27.244235+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02409","citing_title":"The classification of real and bogus transients using active learning and semi-supervised learning","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU","json":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU.json","graph_json":"https://pith.science/api/pith-number/YROZ6QLZFXZIKTLTTMJAW7ZLFU/graph.json","events_json":"https://pith.science/api/pith-number/YROZ6QLZFXZIKTLTTMJAW7ZLFU/events.json","paper":"https://pith.science/paper/YROZ6QLZ"},"agent_actions":{"view_html":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU","download_json":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU.json","view_paper":"https://pith.science/paper/YROZ6QLZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.13877&json=true","fetch_graph":"https://pith.science/api/pith-number/YROZ6QLZFXZIKTLTTMJAW7ZLFU/graph.json","fetch_events":"https://pith.science/api/pith-number/YROZ6QLZFXZIKTLTTMJAW7ZLFU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU/action/storage_attestation","attest_author":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU/action/author_attestation","sign_citation":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU/action/citation_signature","submit_replication":"https://pith.science/pith/YROZ6QLZFXZIKTLTTMJAW7ZLFU/action/replication_record"}},"created_at":"2026-07-05T01:40:27.244235+00:00","updated_at":"2026-07-05T01:40:27.244235+00:00"}