{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:53ZVTXIMTRXB5MUG2WQAXRPBYX","short_pith_number":"pith:53ZVTXIM","schema_version":"1.0","canonical_sha256":"eef359dd0c9c6e1eb286d5a00bc5e1c5ff48f63ae61722f79a5533419ef15009","source":{"kind":"arxiv","id":"2405.10106","version":2},"attestation_state":"computed","paper":{"title":"Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"hep-ex","authors_text":"Benjamin Nachman, Dmitrii Kobylianskii, Eilam Gross, Etienne Dreyer, Nathalie Soybelman, Nilotpal Kakati","submitted_at":"2024-05-16T14:00:55Z","abstract_excerpt":"The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector s"},"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":"2405.10106","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2024-05-16T14:00:55Z","cross_cats_sorted":["hep-ph"],"title_canon_sha256":"d40be8479e4fbb8a5da2daa9b2030f340ea6cdb8a2ff62dcf8a425b00a99faca","abstract_canon_sha256":"7e8dd289c581c21bcf1b9a9c7f8ef9e3eda635da6baa5a55ab92ae61acefdbed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:26.261093Z","signature_b64":"/gvgyV8RVCkKLMCH9gH78l24oL+IGw74JkYLkF0eXiFxKoU1p+kRtduQdKWBqT80E2eOLYKed9luvQpVEU2cDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eef359dd0c9c6e1eb286d5a00bc5e1c5ff48f63ae61722f79a5533419ef15009","last_reissued_at":"2026-07-05T09:38:26.260604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:26.260604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"hep-ex","authors_text":"Benjamin Nachman, Dmitrii Kobylianskii, Eilam Gross, Etienne Dreyer, Nathalie Soybelman, Nilotpal Kakati","submitted_at":"2024-05-16T14:00:55Z","abstract_excerpt":"The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10106","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/2405.10106/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":"2405.10106","created_at":"2026-07-05T09:38:26.260655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.10106v2","created_at":"2026-07-05T09:38:26.260655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10106","created_at":"2026-07-05T09:38:26.260655+00:00"},{"alias_kind":"pith_short_12","alias_value":"53ZVTXIMTRXB","created_at":"2026-07-05T09:38:26.260655+00:00"},{"alias_kind":"pith_short_16","alias_value":"53ZVTXIMTRXB5MUG","created_at":"2026-07-05T09:38:26.260655+00:00"},{"alias_kind":"pith_short_8","alias_value":"53ZVTXIM","created_at":"2026-07-05T09:38:26.260655+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/53ZVTXIMTRXB5MUG2WQAXRPBYX","json":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX.json","graph_json":"https://pith.science/api/pith-number/53ZVTXIMTRXB5MUG2WQAXRPBYX/graph.json","events_json":"https://pith.science/api/pith-number/53ZVTXIMTRXB5MUG2WQAXRPBYX/events.json","paper":"https://pith.science/paper/53ZVTXIM"},"agent_actions":{"view_html":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX","download_json":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX.json","view_paper":"https://pith.science/paper/53ZVTXIM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.10106&json=true","fetch_graph":"https://pith.science/api/pith-number/53ZVTXIMTRXB5MUG2WQAXRPBYX/graph.json","fetch_events":"https://pith.science/api/pith-number/53ZVTXIMTRXB5MUG2WQAXRPBYX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX/action/storage_attestation","attest_author":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX/action/author_attestation","sign_citation":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX/action/citation_signature","submit_replication":"https://pith.science/pith/53ZVTXIMTRXB5MUG2WQAXRPBYX/action/replication_record"}},"created_at":"2026-07-05T09:38:26.260655+00:00","updated_at":"2026-07-05T09:38:26.260655+00:00"}