{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:57MYOA2LEIUKTAYYOSXLTMGEHU","short_pith_number":"pith:57MYOA2L","schema_version":"1.0","canonical_sha256":"efd987034b2228a9831874aeb9b0c43d271e29070af19749779955d5fc60d06b","source":{"kind":"arxiv","id":"2403.12120","version":2},"attestation_state":"computed","paper":{"title":"Light Curve Classification with DistClassiPy: a new distance-based classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.SR","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Ajit Kembhavi, Ashish Mahabal, Federica B. Bianco, Siddharth Chaini","submitted_at":"2024-03-18T18:00:00Z","abstract_excerpt":"The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and de"},"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":"2403.12120","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-03-18T18:00:00Z","cross_cats_sorted":["astro-ph.SR","cs.LG"],"title_canon_sha256":"403068a0fcf28c9fbbb70eb90080dd2b7a5f68c7bf708eaa83064fb820679e75","abstract_canon_sha256":"80c1238f0e1b45a4915f5940a95262fc71fbcefb2774c33e9127ddf41b8a9cd9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:31.421816Z","signature_b64":"CwpX5afB6MRDqaUWYJTLkTOrXEpiPNyox0lUZJvHCp1qMVnxWTPm57wZDAsRt3+Zqn4yzQZLjZrRhXFNbqudAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efd987034b2228a9831874aeb9b0c43d271e29070af19749779955d5fc60d06b","last_reissued_at":"2026-07-05T08:48:31.421406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:31.421406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Light Curve Classification with DistClassiPy: a new distance-based classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.SR","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Ajit Kembhavi, Ashish Mahabal, Federica B. Bianco, Siddharth Chaini","submitted_at":"2024-03-18T18:00:00Z","abstract_excerpt":"The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12120","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/2403.12120/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":"2403.12120","created_at":"2026-07-05T08:48:31.421461+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.12120v2","created_at":"2026-07-05T08:48:31.421461+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12120","created_at":"2026-07-05T08:48:31.421461+00:00"},{"alias_kind":"pith_short_12","alias_value":"57MYOA2LEIUK","created_at":"2026-07-05T08:48:31.421461+00:00"},{"alias_kind":"pith_short_16","alias_value":"57MYOA2LEIUKTAYY","created_at":"2026-07-05T08:48:31.421461+00:00"},{"alias_kind":"pith_short_8","alias_value":"57MYOA2L","created_at":"2026-07-05T08:48:31.421461+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.20574","citing_title":"A Survey on Time-Series Distance Measures","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU","json":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU.json","graph_json":"https://pith.science/api/pith-number/57MYOA2LEIUKTAYYOSXLTMGEHU/graph.json","events_json":"https://pith.science/api/pith-number/57MYOA2LEIUKTAYYOSXLTMGEHU/events.json","paper":"https://pith.science/paper/57MYOA2L"},"agent_actions":{"view_html":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU","download_json":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU.json","view_paper":"https://pith.science/paper/57MYOA2L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.12120&json=true","fetch_graph":"https://pith.science/api/pith-number/57MYOA2LEIUKTAYYOSXLTMGEHU/graph.json","fetch_events":"https://pith.science/api/pith-number/57MYOA2LEIUKTAYYOSXLTMGEHU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU/action/storage_attestation","attest_author":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU/action/author_attestation","sign_citation":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU/action/citation_signature","submit_replication":"https://pith.science/pith/57MYOA2LEIUKTAYYOSXLTMGEHU/action/replication_record"}},"created_at":"2026-07-05T08:48:31.421461+00:00","updated_at":"2026-07-05T08:48:31.421461+00:00"}