{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:B2F4E3ELR4ZB5RXOZXKHW36O7G","short_pith_number":"pith:B2F4E3EL","schema_version":"1.0","canonical_sha256":"0e8bc26c8b8f321ec6eecdd47b6fcef9b72b09980cb84c25d47fb91832e088c8","source":{"kind":"arxiv","id":"1708.08947","version":1},"attestation_state":"computed","paper":{"title":"Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Anais M\\\"oller, Brad E. Tucker, Cas van den Bogaard, Fabian Gieseke, Fang Yuan, Jan van Roestel, Jonas Kindler, Paul J. Groot, Richard A. Scalzo, Steven Bloemen, Tom Heskes, Val\\'erio A.R.M. Ribeiro","submitted_at":"2017-08-29T18:08:56Z","abstract_excerpt":"Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an ap"},"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":"1708.08947","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-08-29T18:08:56Z","cross_cats_sorted":[],"title_canon_sha256":"7d248a6a5f0b562fc16190e18791e080696e8b19ef18a2b103abfcc7afa62eab","abstract_canon_sha256":"72c2e9fe005c71b7b3a64c083bdd85aedd3fa7a1e40fb9c26c6b6de43f9e283a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:21.601326Z","signature_b64":"FNJ+dg0XY9KuqBfMSFR/sylylTVyD5z3pnqGYfg2TCCFbRscABxIj4SRBjEMK/vZGWZO3GMEtaHNGnueqD3IDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e8bc26c8b8f321ec6eecdd47b6fcef9b72b09980cb84c25d47fb91832e088c8","last_reissued_at":"2026-05-18T00:36:21.600622Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:21.600622Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Anais M\\\"oller, Brad E. Tucker, Cas van den Bogaard, Fabian Gieseke, Fang Yuan, Jan van Roestel, Jonas Kindler, Paul J. Groot, Richard A. Scalzo, Steven Bloemen, Tom Heskes, Val\\'erio A.R.M. Ribeiro","submitted_at":"2017-08-29T18:08:56Z","abstract_excerpt":"Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08947","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":"1708.08947","created_at":"2026-05-18T00:36:21.600730+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.08947v1","created_at":"2026-05-18T00:36:21.600730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08947","created_at":"2026-05-18T00:36:21.600730+00:00"},{"alias_kind":"pith_short_12","alias_value":"B2F4E3ELR4ZB","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"B2F4E3ELR4ZB5RXO","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"B2F4E3EL","created_at":"2026-05-18T12:31:08.081275+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/B2F4E3ELR4ZB5RXOZXKHW36O7G","json":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G.json","graph_json":"https://pith.science/api/pith-number/B2F4E3ELR4ZB5RXOZXKHW36O7G/graph.json","events_json":"https://pith.science/api/pith-number/B2F4E3ELR4ZB5RXOZXKHW36O7G/events.json","paper":"https://pith.science/paper/B2F4E3EL"},"agent_actions":{"view_html":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G","download_json":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G.json","view_paper":"https://pith.science/paper/B2F4E3EL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.08947&json=true","fetch_graph":"https://pith.science/api/pith-number/B2F4E3ELR4ZB5RXOZXKHW36O7G/graph.json","fetch_events":"https://pith.science/api/pith-number/B2F4E3ELR4ZB5RXOZXKHW36O7G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G/action/storage_attestation","attest_author":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G/action/author_attestation","sign_citation":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G/action/citation_signature","submit_replication":"https://pith.science/pith/B2F4E3ELR4ZB5RXOZXKHW36O7G/action/replication_record"}},"created_at":"2026-05-18T00:36:21.600730+00:00","updated_at":"2026-05-18T00:36:21.600730+00:00"}