{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FOSOZHKF3BB4JXOELECSC3X5ZV","short_pith_number":"pith:FOSOZHKF","schema_version":"1.0","canonical_sha256":"2ba4ec9d45d843c4ddc45905216efdcd631b1e5b73b6114296f18798cdf9e966","source":{"kind":"arxiv","id":"2406.03233","version":1},"attestation_state":"computed","paper":{"title":"Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","hep-ex"],"primary_cat":"physics.data-an","authors_text":"Jan Dubi\\'nski, Kamil Deja, Miko{\\l}aj Kita, Przemys{\\l}aw Rokita","submitted_at":"2024-06-05T13:11:53Z","abstract_excerpt":"In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simulation methods have garnered attention as promising alternatives to traditional approaches. While existing methods mainly employ Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), recent advancements highlight the efficacy of diffusion models as state-of-the-art generative machine learning methods. We present the first simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on"},"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":"2406.03233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.data-an","submitted_at":"2024-06-05T13:11:53Z","cross_cats_sorted":["cs.CV","hep-ex"],"title_canon_sha256":"4018fb8cc29de6fb81de3345ea33e3c0286cd52c8995fd09c220663331c6f334","abstract_canon_sha256":"9c957ec51df3d9d61e1e3afb055547490dcc000bb14dce0f12ef00de3fcd4480"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:53.516914Z","signature_b64":"/ex2DGpmOMNSVwOkqJ+gdz80YnQvEXAoqJKvhDOy/F+rLLSoNoWyBtAEo5Bt8PfkDo4W4O7SFfI86AliJhspDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ba4ec9d45d843c4ddc45905216efdcd631b1e5b73b6114296f18798cdf9e966","last_reissued_at":"2026-07-05T08:27:53.516492Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:53.516492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","hep-ex"],"primary_cat":"physics.data-an","authors_text":"Jan Dubi\\'nski, Kamil Deja, Miko{\\l}aj Kita, Przemys{\\l}aw Rokita","submitted_at":"2024-06-05T13:11:53Z","abstract_excerpt":"In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simulation methods have garnered attention as promising alternatives to traditional approaches. While existing methods mainly employ Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), recent advancements highlight the efficacy of diffusion models as state-of-the-art generative machine learning methods. We present the first simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03233","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/2406.03233/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":"2406.03233","created_at":"2026-07-05T08:27:53.516552+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.03233v1","created_at":"2026-07-05T08:27:53.516552+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03233","created_at":"2026-07-05T08:27:53.516552+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOSOZHKF3BB4","created_at":"2026-07-05T08:27:53.516552+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOSOZHKF3BB4JXOE","created_at":"2026-07-05T08:27:53.516552+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOSOZHKF","created_at":"2026-07-05T08:27:53.516552+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.20991","citing_title":"ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV","json":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV.json","graph_json":"https://pith.science/api/pith-number/FOSOZHKF3BB4JXOELECSC3X5ZV/graph.json","events_json":"https://pith.science/api/pith-number/FOSOZHKF3BB4JXOELECSC3X5ZV/events.json","paper":"https://pith.science/paper/FOSOZHKF"},"agent_actions":{"view_html":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV","download_json":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV.json","view_paper":"https://pith.science/paper/FOSOZHKF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.03233&json=true","fetch_graph":"https://pith.science/api/pith-number/FOSOZHKF3BB4JXOELECSC3X5ZV/graph.json","fetch_events":"https://pith.science/api/pith-number/FOSOZHKF3BB4JXOELECSC3X5ZV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV/action/storage_attestation","attest_author":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV/action/author_attestation","sign_citation":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV/action/citation_signature","submit_replication":"https://pith.science/pith/FOSOZHKF3BB4JXOELECSC3X5ZV/action/replication_record"}},"created_at":"2026-07-05T08:27:53.516552+00:00","updated_at":"2026-07-05T08:27:53.516552+00:00"}