{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SERNYRI5DDGSLN22ORH7LVUXDZ","short_pith_number":"pith:SERNYRI5","schema_version":"1.0","canonical_sha256":"9122dc451d18cd25b75a744ff5d6971e628cc73bd10f80c398d457cd1ac42b2d","source":{"kind":"arxiv","id":"2302.05481","version":3},"attestation_state":"computed","paper":{"title":"Domain-Adversarial Graph Neural Networks for $\\Lambda$ Hyperon Identification with CLAS12","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"hep-ex","authors_text":"Anselm Vossen, Matthew McEneaney","submitted_at":"2023-02-10T19:32:35Z","abstract_excerpt":"Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. We report on the novel use of GNNs and a domain-adversarial training method to identify $\\Lambda$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the $\\Lambda$ yield by a factor of $1.95$ and by $1.82$ using the domain-adversarial training. This work also provides a good benchmark for developing event tagging machine learning methods for the $\\Lambda$ and other channels at CLAS12 and"},"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":"2302.05481","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"hep-ex","submitted_at":"2023-02-10T19:32:35Z","cross_cats_sorted":["nucl-ex"],"title_canon_sha256":"cc852a0c4886334305f0f3eb2a431ef95b41aefa5784ca99f713fe1a75e2536b","abstract_canon_sha256":"a24b393c4a69712569fcc0a120aa28a43da5bbfaf94f890eb4c97ac6927376a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:00.539906Z","signature_b64":"73aqy2S5nQmRDG8DqRxpRLqQ0rlpTXW+HF7VMiwmqj7/KY6YbTSy+aTHYIKWscskp2rTQcWvrZBTGKq/MzdIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9122dc451d18cd25b75a744ff5d6971e628cc73bd10f80c398d457cd1ac42b2d","last_reissued_at":"2026-07-05T06:46:00.539382Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:00.539382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain-Adversarial Graph Neural Networks for $\\Lambda$ Hyperon Identification with CLAS12","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"hep-ex","authors_text":"Anselm Vossen, Matthew McEneaney","submitted_at":"2023-02-10T19:32:35Z","abstract_excerpt":"Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. We report on the novel use of GNNs and a domain-adversarial training method to identify $\\Lambda$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the $\\Lambda$ yield by a factor of $1.95$ and by $1.82$ using the domain-adversarial training. This work also provides a good benchmark for developing event tagging machine learning methods for the $\\Lambda$ and other channels at CLAS12 and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05481","kind":"arxiv","version":3},"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/2302.05481/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":"2302.05481","created_at":"2026-07-05T06:46:00.539437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.05481v3","created_at":"2026-07-05T06:46:00.539437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05481","created_at":"2026-07-05T06:46:00.539437+00:00"},{"alias_kind":"pith_short_12","alias_value":"SERNYRI5DDGS","created_at":"2026-07-05T06:46:00.539437+00:00"},{"alias_kind":"pith_short_16","alias_value":"SERNYRI5DDGSLN22","created_at":"2026-07-05T06:46:00.539437+00:00"},{"alias_kind":"pith_short_8","alias_value":"SERNYRI5","created_at":"2026-07-05T06:46:00.539437+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/SERNYRI5DDGSLN22ORH7LVUXDZ","json":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ.json","graph_json":"https://pith.science/api/pith-number/SERNYRI5DDGSLN22ORH7LVUXDZ/graph.json","events_json":"https://pith.science/api/pith-number/SERNYRI5DDGSLN22ORH7LVUXDZ/events.json","paper":"https://pith.science/paper/SERNYRI5"},"agent_actions":{"view_html":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ","download_json":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ.json","view_paper":"https://pith.science/paper/SERNYRI5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.05481&json=true","fetch_graph":"https://pith.science/api/pith-number/SERNYRI5DDGSLN22ORH7LVUXDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/SERNYRI5DDGSLN22ORH7LVUXDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ/action/storage_attestation","attest_author":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ/action/author_attestation","sign_citation":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ/action/citation_signature","submit_replication":"https://pith.science/pith/SERNYRI5DDGSLN22ORH7LVUXDZ/action/replication_record"}},"created_at":"2026-07-05T06:46:00.539437+00:00","updated_at":"2026-07-05T06:46:00.539437+00:00"}