{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HFOVD4VHWW4S7ISJMYI25PQDCM","short_pith_number":"pith:HFOVD4VH","schema_version":"1.0","canonical_sha256":"395d51f2a7b5b92fa2496611aebe03130fbc083f8accfd097e0784ef084bcf63","source":{"kind":"arxiv","id":"2408.01486","version":2},"attestation_state":"computed","paper":{"title":"Differentiable MadNIS-Lite","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.comp-ph"],"primary_cat":"hep-ph","authors_text":"Olivier Mattelaer, Ramon Winterhalder, Theo Heimel, Tilman Plehn","submitted_at":"2024-08-02T18:00:00Z","abstract_excerpt":"Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS."},"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":"2408.01486","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2024-08-02T18:00:00Z","cross_cats_sorted":["hep-ex","physics.comp-ph"],"title_canon_sha256":"ab5bcdd1dee58fa4f8afc77a61d67d9c193e7908701f002f9c7222fa45bf849b","abstract_canon_sha256":"a1be0ac7d41dd542e9db5a31f536595ac538336288fd007ecbbaa2b64871413b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:39.693052Z","signature_b64":"/Nsql3MAzUVYkQlM31kEQpyK7Vo/USgSDdejEWo9EQ2afLH9jENruPaUegU96dSXmKY51QZ+obL1ifP7si10Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"395d51f2a7b5b92fa2496611aebe03130fbc083f8accfd097e0784ef084bcf63","last_reissued_at":"2026-07-05T10:00:39.692568Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:39.692568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentiable MadNIS-Lite","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.comp-ph"],"primary_cat":"hep-ph","authors_text":"Olivier Mattelaer, Ramon Winterhalder, Theo Heimel, Tilman Plehn","submitted_at":"2024-08-02T18:00:00Z","abstract_excerpt":"Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.01486","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/2408.01486/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":"2408.01486","created_at":"2026-07-05T10:00:39.692630+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.01486v2","created_at":"2026-07-05T10:00:39.692630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.01486","created_at":"2026-07-05T10:00:39.692630+00:00"},{"alias_kind":"pith_short_12","alias_value":"HFOVD4VHWW4S","created_at":"2026-07-05T10:00:39.692630+00:00"},{"alias_kind":"pith_short_16","alias_value":"HFOVD4VHWW4S7ISJ","created_at":"2026-07-05T10:00:39.692630+00:00"},{"alias_kind":"pith_short_8","alias_value":"HFOVD4VH","created_at":"2026-07-05T10:00:39.692630+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.00155","citing_title":"Amplitude Uncertainties Everywhere All at Once","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02415","citing_title":"Generative models on phase space","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03511","citing_title":"Monte Carlo Event Generation with Continuous Normalizing Flows","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM","json":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM.json","graph_json":"https://pith.science/api/pith-number/HFOVD4VHWW4S7ISJMYI25PQDCM/graph.json","events_json":"https://pith.science/api/pith-number/HFOVD4VHWW4S7ISJMYI25PQDCM/events.json","paper":"https://pith.science/paper/HFOVD4VH"},"agent_actions":{"view_html":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM","download_json":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM.json","view_paper":"https://pith.science/paper/HFOVD4VH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.01486&json=true","fetch_graph":"https://pith.science/api/pith-number/HFOVD4VHWW4S7ISJMYI25PQDCM/graph.json","fetch_events":"https://pith.science/api/pith-number/HFOVD4VHWW4S7ISJMYI25PQDCM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM/action/storage_attestation","attest_author":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM/action/author_attestation","sign_citation":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM/action/citation_signature","submit_replication":"https://pith.science/pith/HFOVD4VHWW4S7ISJMYI25PQDCM/action/replication_record"}},"created_at":"2026-07-05T10:00:39.692630+00:00","updated_at":"2026-07-05T10:00:39.692630+00:00"}