{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:5FI2WLGOODVBXONUVNA3HRDVNP","short_pith_number":"pith:5FI2WLGO","schema_version":"1.0","canonical_sha256":"e951ab2cce70ea1bb9b4ab41b3c4756bffb140a3f9eeeb6f3ce8e3e2ecb8e625","source":{"kind":"arxiv","id":"2109.00546","version":3},"attestation_state":"computed","paper":{"title":"Classifying Anomalies THrough Outer Density Estimation (CATHODE)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"hep-ph","authors_text":"Anna Hallin, Benjamin Nachman, Claudius Krause, David Shih, Gregor Kasieczka, Joshua Isaacson, Manuel Sommerhalder, Matthias Schlaffer, Tobias Quadfasel","submitted_at":"2021-09-01T18:00:00Z","abstract_excerpt":"We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection. Our approach, which we call Classifying Anomalies THrough Outer Density Estimation (CATHODE), assumes the BSM signal is localized in a signal region (defined e.g. using invariant mass). By training a conditional density estimator on a collection of additional features outside the signal region, interpolating it into the signal region, and sampling from it, we produce a collection of events that follow the backgro"},"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":"2109.00546","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2021-09-01T18:00:00Z","cross_cats_sorted":["hep-ex","physics.data-an"],"title_canon_sha256":"7b519a58367dda78fd1e004c0d235087b386f1d0f4231893a303153414f9a51d","abstract_canon_sha256":"1874bea49634be9e839cf476b402690c1c58242ad2fc9e719f845575ec9ea7e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:11.739459Z","signature_b64":"f/M6x2q80oovIfot93NMHYt+qPtqyJH01anVRToNsF1JAfT/kvTyhV6FzFWHhodO0Wf7QVSFnoM43iDjuNWSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e951ab2cce70ea1bb9b4ab41b3c4756bffb140a3f9eeeb6f3ce8e3e2ecb8e625","last_reissued_at":"2026-07-05T04:56:11.738956Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:11.738956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classifying Anomalies THrough Outer Density Estimation (CATHODE)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"hep-ph","authors_text":"Anna Hallin, Benjamin Nachman, Claudius Krause, David Shih, Gregor Kasieczka, Joshua Isaacson, Manuel Sommerhalder, Matthias Schlaffer, Tobias Quadfasel","submitted_at":"2021-09-01T18:00:00Z","abstract_excerpt":"We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection. Our approach, which we call Classifying Anomalies THrough Outer Density Estimation (CATHODE), assumes the BSM signal is localized in a signal region (defined e.g. using invariant mass). By training a conditional density estimator on a collection of additional features outside the signal region, interpolating it into the signal region, and sampling from it, we produce a collection of events that follow the backgro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00546","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/2109.00546/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":"2109.00546","created_at":"2026-07-05T04:56:11.739015+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.00546v3","created_at":"2026-07-05T04:56:11.739015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00546","created_at":"2026-07-05T04:56:11.739015+00:00"},{"alias_kind":"pith_short_12","alias_value":"5FI2WLGOODVB","created_at":"2026-07-05T04:56:11.739015+00:00"},{"alias_kind":"pith_short_16","alias_value":"5FI2WLGOODVBXONU","created_at":"2026-07-05T04:56:11.739015+00:00"},{"alias_kind":"pith_short_8","alias_value":"5FI2WLGO","created_at":"2026-07-05T04:56:11.739015+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07158","citing_title":"Weakly supervised machine learning for model-agnostic searches of new phenomena in the $\\gamma$-ray sky","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11071","citing_title":"Time-dependent signals of new physics at the LHC","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20965","citing_title":"Kitchen Sink Anomaly Detection","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP","json":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP.json","graph_json":"https://pith.science/api/pith-number/5FI2WLGOODVBXONUVNA3HRDVNP/graph.json","events_json":"https://pith.science/api/pith-number/5FI2WLGOODVBXONUVNA3HRDVNP/events.json","paper":"https://pith.science/paper/5FI2WLGO"},"agent_actions":{"view_html":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP","download_json":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP.json","view_paper":"https://pith.science/paper/5FI2WLGO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.00546&json=true","fetch_graph":"https://pith.science/api/pith-number/5FI2WLGOODVBXONUVNA3HRDVNP/graph.json","fetch_events":"https://pith.science/api/pith-number/5FI2WLGOODVBXONUVNA3HRDVNP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP/action/storage_attestation","attest_author":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP/action/author_attestation","sign_citation":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP/action/citation_signature","submit_replication":"https://pith.science/pith/5FI2WLGOODVBXONUVNA3HRDVNP/action/replication_record"}},"created_at":"2026-07-05T04:56:11.739015+00:00","updated_at":"2026-07-05T04:56:11.739015+00:00"}