{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WQI6ZLI4UG4YIR4EBK74LIR4OJ","short_pith_number":"pith:WQI6ZLI4","schema_version":"1.0","canonical_sha256":"b411ecad1ca1b98447840abfc5a23c7268836074bec7533674babfeb8dc035a2","source":{"kind":"arxiv","id":"2402.11538","version":1},"attestation_state":"computed","paper":{"title":"PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"hep-ph","authors_text":"Dmitrii Kobylianski, Eilam Gross, Etienne Dreyer, Junjian Lu, Shangsong Liang, Siwei Liu","submitted_at":"2024-02-18T10:38:34Z","abstract_excerpt":"In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations (LCA"},"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":"2402.11538","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2024-02-18T10:38:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5c37121714d0c15205b27efc80e637748a6886da20ddf4bc6ad187c0f6965206","abstract_canon_sha256":"ffd54183ec54bd0d221e3fd346bf8b189fad35b8c8c2cfecf11cf4c059a61fd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:24.491185Z","signature_b64":"vArzAaZE3q2zT+nYrUa1vyv2t80odZ179eNRPtI3I/bmV9A1pcTNXMsXQVII7cCcJ/i4Wqt061DSLvWLfGBYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b411ecad1ca1b98447840abfc5a23c7268836074bec7533674babfeb8dc035a2","last_reissued_at":"2026-07-05T09:38:24.490703Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:24.490703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"hep-ph","authors_text":"Dmitrii Kobylianski, Eilam Gross, Etienne Dreyer, Junjian Lu, Shangsong Liang, Siwei Liu","submitted_at":"2024-02-18T10:38:34Z","abstract_excerpt":"In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations (LCA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11538","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/2402.11538/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":"2402.11538","created_at":"2026-07-05T09:38:24.490763+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11538v1","created_at":"2026-07-05T09:38:24.490763+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11538","created_at":"2026-07-05T09:38:24.490763+00:00"},{"alias_kind":"pith_short_12","alias_value":"WQI6ZLI4UG4Y","created_at":"2026-07-05T09:38:24.490763+00:00"},{"alias_kind":"pith_short_16","alias_value":"WQI6ZLI4UG4YIR4E","created_at":"2026-07-05T09:38:24.490763+00:00"},{"alias_kind":"pith_short_8","alias_value":"WQI6ZLI4","created_at":"2026-07-05T09:38:24.490763+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.10039","citing_title":"Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ","json":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ.json","graph_json":"https://pith.science/api/pith-number/WQI6ZLI4UG4YIR4EBK74LIR4OJ/graph.json","events_json":"https://pith.science/api/pith-number/WQI6ZLI4UG4YIR4EBK74LIR4OJ/events.json","paper":"https://pith.science/paper/WQI6ZLI4"},"agent_actions":{"view_html":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ","download_json":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ.json","view_paper":"https://pith.science/paper/WQI6ZLI4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11538&json=true","fetch_graph":"https://pith.science/api/pith-number/WQI6ZLI4UG4YIR4EBK74LIR4OJ/graph.json","fetch_events":"https://pith.science/api/pith-number/WQI6ZLI4UG4YIR4EBK74LIR4OJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ/action/storage_attestation","attest_author":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ/action/author_attestation","sign_citation":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ/action/citation_signature","submit_replication":"https://pith.science/pith/WQI6ZLI4UG4YIR4EBK74LIR4OJ/action/replication_record"}},"created_at":"2026-07-05T09:38:24.490763+00:00","updated_at":"2026-07-05T09:38:24.490763+00:00"}