{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2UN3YXTPK4RRSHSV2FL7C4EXIO","short_pith_number":"pith:2UN3YXTP","schema_version":"1.0","canonical_sha256":"d51bbc5e6f5723191e55d157f17097439bc9df003727c3829279559a0dfa1476","source":{"kind":"arxiv","id":"2502.17990","version":1},"attestation_state":"computed","paper":{"title":"Statistical Analyses of Solar Active Region in SDO/HMI Magnetograms detected by Unsupervised Machine Learning Method DSARD","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","physics.space-ph"],"primary_cat":"astro-ph.SR","authors_text":"Chenxi Shi, Pengfei Chen, Qi Hao, Ruishuo Chen, Wutong Lu, Yifan Meng","submitted_at":"2025-02-25T08:57:25Z","abstract_excerpt":"Solar active regions (ARs) are the places hosting the majority of solar eruptions. Studying the evolution and morphological features of ARs is not only of great significance to the understanding of the physical mechanisms of solar eruptions, but also beneficial for the hazardous space weather forecast. An automated DBSCAN-based Solar Active Regions Detection (DSARD) method for solar ARs observed in magnetograms is developed in this work, which is based on an unsupervised machine learning algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The method is then e"},"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":"2502.17990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.SR","submitted_at":"2025-02-25T08:57:25Z","cross_cats_sorted":["astro-ph.IM","physics.space-ph"],"title_canon_sha256":"e6b6b9e39a1b945c71684eee824c3ff69a2f877c71a5f3d86556fa94b4b340e4","abstract_canon_sha256":"e71c9de44dec692f575e9d364edc7796c9aecde97e410375838a6134c041d3c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:47.539826Z","signature_b64":"iL0EkSuP5S5WQgOywenXs4CWz8sDRWzz/Q3O3ksup/Q3lPmDjLdXWCQ4oqR7XmwiHSmlXt3ngyfXVDRvqRy/Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d51bbc5e6f5723191e55d157f17097439bc9df003727c3829279559a0dfa1476","last_reissued_at":"2026-07-05T10:19:47.539342Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:47.539342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical Analyses of Solar Active Region in SDO/HMI Magnetograms detected by Unsupervised Machine Learning Method DSARD","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","physics.space-ph"],"primary_cat":"astro-ph.SR","authors_text":"Chenxi Shi, Pengfei Chen, Qi Hao, Ruishuo Chen, Wutong Lu, Yifan Meng","submitted_at":"2025-02-25T08:57:25Z","abstract_excerpt":"Solar active regions (ARs) are the places hosting the majority of solar eruptions. Studying the evolution and morphological features of ARs is not only of great significance to the understanding of the physical mechanisms of solar eruptions, but also beneficial for the hazardous space weather forecast. An automated DBSCAN-based Solar Active Regions Detection (DSARD) method for solar ARs observed in magnetograms is developed in this work, which is based on an unsupervised machine learning algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The method is then e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17990","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.17990/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":"2502.17990","created_at":"2026-07-05T10:19:47.539401+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.17990v1","created_at":"2026-07-05T10:19:47.539401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17990","created_at":"2026-07-05T10:19:47.539401+00:00"},{"alias_kind":"pith_short_12","alias_value":"2UN3YXTPK4RR","created_at":"2026-07-05T10:19:47.539401+00:00"},{"alias_kind":"pith_short_16","alias_value":"2UN3YXTPK4RRSHSV","created_at":"2026-07-05T10:19:47.539401+00:00"},{"alias_kind":"pith_short_8","alias_value":"2UN3YXTP","created_at":"2026-07-05T10:19:47.539401+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04991","citing_title":"Deep Learning with Magnetic Parameter Constraints for Short-Term Prediction of Solar Active Region Vector Magnetic Fields","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO","json":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO.json","graph_json":"https://pith.science/api/pith-number/2UN3YXTPK4RRSHSV2FL7C4EXIO/graph.json","events_json":"https://pith.science/api/pith-number/2UN3YXTPK4RRSHSV2FL7C4EXIO/events.json","paper":"https://pith.science/paper/2UN3YXTP"},"agent_actions":{"view_html":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO","download_json":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO.json","view_paper":"https://pith.science/paper/2UN3YXTP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.17990&json=true","fetch_graph":"https://pith.science/api/pith-number/2UN3YXTPK4RRSHSV2FL7C4EXIO/graph.json","fetch_events":"https://pith.science/api/pith-number/2UN3YXTPK4RRSHSV2FL7C4EXIO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO/action/storage_attestation","attest_author":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO/action/author_attestation","sign_citation":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO/action/citation_signature","submit_replication":"https://pith.science/pith/2UN3YXTPK4RRSHSV2FL7C4EXIO/action/replication_record"}},"created_at":"2026-07-05T10:19:47.539401+00:00","updated_at":"2026-07-05T10:19:47.539401+00:00"}