{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BTKMQASRVCEOU7YZRVNN3XIHED","short_pith_number":"pith:BTKMQASR","schema_version":"1.0","canonical_sha256":"0cd4c80251a888ea7f198d5adddd0720d5100555bcea05ff33217bfcabc72057","source":{"kind":"arxiv","id":"2308.14288","version":4},"attestation_state":"computed","paper":{"title":"GRB Optical and X-ray Plateau Properties Classifier Using Unsupervised Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"Aditya Narendra, Agnieszka Pollo, Anish Kalsi, Enrico Rinaldi, Maria G. Dainotti, Sachin Venkatesh, Shubham Bhardwaj","submitted_at":"2023-08-28T04:02:04Z","abstract_excerpt":"The division of Gamma-ray bursts (GRBs) into different classes, other than the \"short\" and \"long\", has been an active field of research. We investigate whether GRBs can be classified based on a broader set of parameters, including prompt and plateau emission ones. Observational evidence suggests the existence of more GRB sub-classes, but results so far are either conflicting or not statistically significant. The novelty here is producing a machine-learning-based classification of GRBs using their observed X-rays and optical properties. We used two data samples: the first, composed of 203 GRBs,"},"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":"2308.14288","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.HE","submitted_at":"2023-08-28T04:02:04Z","cross_cats_sorted":[],"title_canon_sha256":"19e9e499514fc8951c6faebefe7a71d9f376504ace0ab079f2357b691389e94e","abstract_canon_sha256":"9ef34a7f09954b93a4e2e96d6ac174c89eee385d562e1e54e322efaf0fce8920"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:45.684550Z","signature_b64":"IzmONzzgkTHXB3h321erQ7NNup95qzW2mTG1WNPKFmIsW+LmzKjAafaRsAdL4cYIlOz6GuqzsiS6irEVOR7nBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cd4c80251a888ea7f198d5adddd0720d5100555bcea05ff33217bfcabc72057","last_reissued_at":"2026-07-05T07:16:45.684059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:45.684059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GRB Optical and X-ray Plateau Properties Classifier Using Unsupervised Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"Aditya Narendra, Agnieszka Pollo, Anish Kalsi, Enrico Rinaldi, Maria G. Dainotti, Sachin Venkatesh, Shubham Bhardwaj","submitted_at":"2023-08-28T04:02:04Z","abstract_excerpt":"The division of Gamma-ray bursts (GRBs) into different classes, other than the \"short\" and \"long\", has been an active field of research. We investigate whether GRBs can be classified based on a broader set of parameters, including prompt and plateau emission ones. Observational evidence suggests the existence of more GRB sub-classes, but results so far are either conflicting or not statistically significant. The novelty here is producing a machine-learning-based classification of GRBs using their observed X-rays and optical properties. We used two data samples: the first, composed of 203 GRBs,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.14288","kind":"arxiv","version":4},"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/2308.14288/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":"2308.14288","created_at":"2026-07-05T07:16:45.684116+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.14288v4","created_at":"2026-07-05T07:16:45.684116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.14288","created_at":"2026-07-05T07:16:45.684116+00:00"},{"alias_kind":"pith_short_12","alias_value":"BTKMQASRVCEO","created_at":"2026-07-05T07:16:45.684116+00:00"},{"alias_kind":"pith_short_16","alias_value":"BTKMQASRVCEOU7YZ","created_at":"2026-07-05T07:16:45.684116+00:00"},{"alias_kind":"pith_short_8","alias_value":"BTKMQASR","created_at":"2026-07-05T07:16:45.684116+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/BTKMQASRVCEOU7YZRVNN3XIHED","json":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED.json","graph_json":"https://pith.science/api/pith-number/BTKMQASRVCEOU7YZRVNN3XIHED/graph.json","events_json":"https://pith.science/api/pith-number/BTKMQASRVCEOU7YZRVNN3XIHED/events.json","paper":"https://pith.science/paper/BTKMQASR"},"agent_actions":{"view_html":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED","download_json":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED.json","view_paper":"https://pith.science/paper/BTKMQASR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.14288&json=true","fetch_graph":"https://pith.science/api/pith-number/BTKMQASRVCEOU7YZRVNN3XIHED/graph.json","fetch_events":"https://pith.science/api/pith-number/BTKMQASRVCEOU7YZRVNN3XIHED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED/action/storage_attestation","attest_author":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED/action/author_attestation","sign_citation":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED/action/citation_signature","submit_replication":"https://pith.science/pith/BTKMQASRVCEOU7YZRVNN3XIHED/action/replication_record"}},"created_at":"2026-07-05T07:16:45.684116+00:00","updated_at":"2026-07-05T07:16:45.684116+00:00"}