{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:33UQCEO3OVLHNSZS533KA2TFQO","short_pith_number":"pith:33UQCEO3","schema_version":"1.0","canonical_sha256":"dee90111db755676cb32eef6a06a6583ae5df3457454f3a36e0ff04a39679a5a","source":{"kind":"arxiv","id":"2510.02168","version":2},"attestation_state":"computed","paper":{"title":"Wasserstein normalized autoencoder for anomaly detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"hep-ex","authors_text":"CMS Collaboration","submitted_at":"2025-10-02T16:15:48Z","abstract_excerpt":"A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution--a Boltzmann distribution where the energy is the reconstruction error of the autoencoder--and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets--conical sprays of visible standard model particles and invisible dark matter states--with the CMS experiment at the CERN LHC. Trained on jets of particles from "},"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":"2510.02168","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2025-10-02T16:15:48Z","cross_cats_sorted":["physics.data-an"],"title_canon_sha256":"40b6f2d7240af815572b3e99ae79c7092b61c115d2cc102c4a5f7b61f309fe26","abstract_canon_sha256":"0cdefcc2c66ffb4a21c72d8165ab827c0023d7016106f7b907af7b5e545fa559"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:02:22.439866Z","signature_b64":"IiTkpi1KW/4Ojx7oovj9ZUikoIYqIfFvt6TRGldldtPu9ioliVpmEgyYQGQodJf8OpE52OXsPWoyv9pIjzn+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dee90111db755676cb32eef6a06a6583ae5df3457454f3a36e0ff04a39679a5a","last_reissued_at":"2026-06-01T01:02:22.438819Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:02:22.438819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wasserstein normalized autoencoder for anomaly detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"hep-ex","authors_text":"CMS Collaboration","submitted_at":"2025-10-02T16:15:48Z","abstract_excerpt":"A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution--a Boltzmann distribution where the energy is the reconstruction error of the autoencoder--and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets--conical sprays of visible standard model particles and invisible dark matter states--with the CMS experiment at the CERN LHC. Trained on jets of particles from "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.02168","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/2510.02168/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":"2510.02168","created_at":"2026-06-01T01:02:22.438977+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.02168v2","created_at":"2026-06-01T01:02:22.438977+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.02168","created_at":"2026-06-01T01:02:22.438977+00:00"},{"alias_kind":"pith_short_12","alias_value":"33UQCEO3OVLH","created_at":"2026-06-01T01:02:22.438977+00:00"},{"alias_kind":"pith_short_16","alias_value":"33UQCEO3OVLHNSZS","created_at":"2026-06-01T01:02:22.438977+00:00"},{"alias_kind":"pith_short_8","alias_value":"33UQCEO3","created_at":"2026-06-01T01:02:22.438977+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/33UQCEO3OVLHNSZS533KA2TFQO","json":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO.json","graph_json":"https://pith.science/api/pith-number/33UQCEO3OVLHNSZS533KA2TFQO/graph.json","events_json":"https://pith.science/api/pith-number/33UQCEO3OVLHNSZS533KA2TFQO/events.json","paper":"https://pith.science/paper/33UQCEO3"},"agent_actions":{"view_html":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO","download_json":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO.json","view_paper":"https://pith.science/paper/33UQCEO3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.02168&json=true","fetch_graph":"https://pith.science/api/pith-number/33UQCEO3OVLHNSZS533KA2TFQO/graph.json","fetch_events":"https://pith.science/api/pith-number/33UQCEO3OVLHNSZS533KA2TFQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO/action/storage_attestation","attest_author":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO/action/author_attestation","sign_citation":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO/action/citation_signature","submit_replication":"https://pith.science/pith/33UQCEO3OVLHNSZS533KA2TFQO/action/replication_record"}},"created_at":"2026-06-01T01:02:22.438977+00:00","updated_at":"2026-06-01T01:02:22.438977+00:00"}