{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MNJAVSUYKWCCA22DF4SY5KC6CP","short_pith_number":"pith:MNJAVSUY","schema_version":"1.0","canonical_sha256":"63520aca985584206b432f258ea85e13d0bf77c5d3b9a15ac8ee22645539f919","source":{"kind":"arxiv","id":"2312.11618","version":2},"attestation_state":"computed","paper":{"title":"Anomaly detection with flow-based fast calorimeter simulators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","hep-ex","physics.data-an","physics.ins-det"],"primary_cat":"hep-ph","authors_text":"Benjamin Nachman, Claudius Krause, David Shih, Ian Pang, Yunhao Zhu","submitted_at":"2023-12-18T19:00:01Z","abstract_excerpt":"Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The CaloFlow model is designed to generate single photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being"},"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":"2312.11618","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2023-12-18T19:00:01Z","cross_cats_sorted":["astro-ph.IM","hep-ex","physics.data-an","physics.ins-det"],"title_canon_sha256":"e2a9a51546789f31188bf6bb925e8c6f4245ea6457e712323b4eb7eada6415a6","abstract_canon_sha256":"66d2ff9cfb307c5471c0ad619fa9e7828d5cd9deae880763b06630a2cbb2e2e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:33.013033Z","signature_b64":"M0Sdb14Z2BKsbXIeH5Qg3Hf3/+qciZ1VlTL/nHSCjI3JgxHCnrvhMiRh2w407xOUECcugJTeJPGIjwqrKwTICw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63520aca985584206b432f258ea85e13d0bf77c5d3b9a15ac8ee22645539f919","last_reissued_at":"2026-07-05T09:05:33.012524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:33.012524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Anomaly detection with flow-based fast calorimeter simulators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","hep-ex","physics.data-an","physics.ins-det"],"primary_cat":"hep-ph","authors_text":"Benjamin Nachman, Claudius Krause, David Shih, Ian Pang, Yunhao Zhu","submitted_at":"2023-12-18T19:00:01Z","abstract_excerpt":"Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The CaloFlow model is designed to generate single photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11618","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/2312.11618/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":"2312.11618","created_at":"2026-07-05T09:05:33.012598+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.11618v2","created_at":"2026-07-05T09:05:33.012598+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11618","created_at":"2026-07-05T09:05:33.012598+00:00"},{"alias_kind":"pith_short_12","alias_value":"MNJAVSUYKWCC","created_at":"2026-07-05T09:05:33.012598+00:00"},{"alias_kind":"pith_short_16","alias_value":"MNJAVSUYKWCCA22D","created_at":"2026-07-05T09:05:33.012598+00:00"},{"alias_kind":"pith_short_8","alias_value":"MNJAVSUY","created_at":"2026-07-05T09:05:33.012598+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/MNJAVSUYKWCCA22DF4SY5KC6CP","json":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP.json","graph_json":"https://pith.science/api/pith-number/MNJAVSUYKWCCA22DF4SY5KC6CP/graph.json","events_json":"https://pith.science/api/pith-number/MNJAVSUYKWCCA22DF4SY5KC6CP/events.json","paper":"https://pith.science/paper/MNJAVSUY"},"agent_actions":{"view_html":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP","download_json":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP.json","view_paper":"https://pith.science/paper/MNJAVSUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.11618&json=true","fetch_graph":"https://pith.science/api/pith-number/MNJAVSUYKWCCA22DF4SY5KC6CP/graph.json","fetch_events":"https://pith.science/api/pith-number/MNJAVSUYKWCCA22DF4SY5KC6CP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP/action/storage_attestation","attest_author":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP/action/author_attestation","sign_citation":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP/action/citation_signature","submit_replication":"https://pith.science/pith/MNJAVSUYKWCCA22DF4SY5KC6CP/action/replication_record"}},"created_at":"2026-07-05T09:05:33.012598+00:00","updated_at":"2026-07-05T09:05:33.012598+00:00"}