{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2BDWVOAOMQRQ2WK36GORMRAS4H","short_pith_number":"pith:2BDWVOAO","schema_version":"1.0","canonical_sha256":"d0476ab80e64230d595bf19d164412e1f7e7bc96da4f6d4cb721cf13a6154752","source":{"kind":"arxiv","id":"2110.11377","version":2},"attestation_state":"computed","paper":{"title":"CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","hep-ph","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Claudius Krause, David Shih","submitted_at":"2021-10-21T18:00:04Z","abstract_excerpt":"Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present CaloFlow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original. The improvement is based on a technique called Probability Density Distillation, originally developed for speech synthesis in the ML literature, and which we develop further by introducing a set of powerful new loss terms. We demonstrate that CaloFlow v2 preserves the same high fidelity of the original usin"},"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":"2110.11377","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ins-det","submitted_at":"2021-10-21T18:00:04Z","cross_cats_sorted":["cs.LG","hep-ex","hep-ph","physics.data-an"],"title_canon_sha256":"c2f8b4ddc8337a902273d1b1947042b7876eb4c3334e789a34cc545b0c45b531","abstract_canon_sha256":"22bb1e8b8287bfaa9f13e20d07c3c0e4800e66f6be0dc79f547486135862daa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:07:19.559485Z","signature_b64":"r9I3V4DTeZFG58B7P4+RFwky0oyeCRBeW+u79woVlpgEOrcav7dFxKVvy2ig6BZGL9TAO3ITqmGvHzqzVODLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0476ab80e64230d595bf19d164412e1f7e7bc96da4f6d4cb721cf13a6154752","last_reissued_at":"2026-07-05T06:07:19.558982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:07:19.558982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","hep-ph","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Claudius Krause, David Shih","submitted_at":"2021-10-21T18:00:04Z","abstract_excerpt":"Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present CaloFlow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original. The improvement is based on a technique called Probability Density Distillation, originally developed for speech synthesis in the ML literature, and which we develop further by introducing a set of powerful new loss terms. We demonstrate that CaloFlow v2 preserves the same high fidelity of the original usin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11377","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/2110.11377/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":"2110.11377","created_at":"2026-07-05T06:07:19.559035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.11377v2","created_at":"2026-07-05T06:07:19.559035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11377","created_at":"2026-07-05T06:07:19.559035+00:00"},{"alias_kind":"pith_short_12","alias_value":"2BDWVOAOMQRQ","created_at":"2026-07-05T06:07:19.559035+00:00"},{"alias_kind":"pith_short_16","alias_value":"2BDWVOAOMQRQ2WK3","created_at":"2026-07-05T06:07:19.559035+00:00"},{"alias_kind":"pith_short_8","alias_value":"2BDWVOAO","created_at":"2026-07-05T06:07:19.559035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11304","citing_title":"SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2512.04153","citing_title":"Data-Driven Predictions for Dark Photon and Millicharged Particle Production","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17511","citing_title":"Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model","ref_index":99,"is_internal_anchor":false},{"citing_arxiv_id":"2509.00155","citing_title":"Amplitude Uncertainties Everywhere All at Once","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2601.07859","citing_title":"Differentiable Surrogate for Detector Simulation and Design with Diffusion Models","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07565","citing_title":"Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context","ref_index":120,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H","json":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H.json","graph_json":"https://pith.science/api/pith-number/2BDWVOAOMQRQ2WK36GORMRAS4H/graph.json","events_json":"https://pith.science/api/pith-number/2BDWVOAOMQRQ2WK36GORMRAS4H/events.json","paper":"https://pith.science/paper/2BDWVOAO"},"agent_actions":{"view_html":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H","download_json":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H.json","view_paper":"https://pith.science/paper/2BDWVOAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.11377&json=true","fetch_graph":"https://pith.science/api/pith-number/2BDWVOAOMQRQ2WK36GORMRAS4H/graph.json","fetch_events":"https://pith.science/api/pith-number/2BDWVOAOMQRQ2WK36GORMRAS4H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H/action/storage_attestation","attest_author":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H/action/author_attestation","sign_citation":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H/action/citation_signature","submit_replication":"https://pith.science/pith/2BDWVOAOMQRQ2WK36GORMRAS4H/action/replication_record"}},"created_at":"2026-07-05T06:07:19.559035+00:00","updated_at":"2026-07-05T06:07:19.559035+00:00"}