{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OY575D4NXOSSYFZLFEUQ64H7IY","short_pith_number":"pith:OY575D4N","schema_version":"1.0","canonical_sha256":"763bfe8f8dbba52c172b29290f70ff460c8de769175ca815f8c45c5821b04e5e","source":{"kind":"arxiv","id":"1907.10621","version":2},"attestation_state":"computed","paper":{"title":"MadMiner: Machine learning-based inference for particle physics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.data-an","stat.ML"],"primary_cat":"hep-ph","authors_text":"Felix Kling, Irina Espejo, Johann Brehmer, Kyle Cranmer","submitted_at":"2019-07-24T18:00:02Z","abstract_excerpt":"Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper we introduce MadMiner, a Python module that streamlines the steps involved in this procedure. Wr"},"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":"1907.10621","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2019-07-24T18:00:02Z","cross_cats_sorted":["hep-ex","physics.data-an","stat.ML"],"title_canon_sha256":"10faac5b2a6f0a3608370f1d00d1b248c5ac2b19281109b8c9e1c38f2d11f976","abstract_canon_sha256":"0bdbf28ffbb8cbd0ce514ae07a0a0973f2f5e201a6a5af225f4b1d38caeecbf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:34:16.452212Z","signature_b64":"DQDzqK3pYEmNnfY7vUrBJ36YPAwIuszLlQcgKP8r/zBgEBHgCvR5mBRdbcHOzm2fJmQdjNjMlbSykkwDgX4NAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"763bfe8f8dbba52c172b29290f70ff460c8de769175ca815f8c45c5821b04e5e","last_reissued_at":"2026-07-05T00:34:16.451748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:34:16.451748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MadMiner: Machine learning-based inference for particle physics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.data-an","stat.ML"],"primary_cat":"hep-ph","authors_text":"Felix Kling, Irina Espejo, Johann Brehmer, Kyle Cranmer","submitted_at":"2019-07-24T18:00:02Z","abstract_excerpt":"Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper we introduce MadMiner, a Python module that streamlines the steps involved in this procedure. Wr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10621","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/1907.10621/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":"1907.10621","created_at":"2026-07-05T00:34:16.451804+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10621v2","created_at":"2026-07-05T00:34:16.451804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10621","created_at":"2026-07-05T00:34:16.451804+00:00"},{"alias_kind":"pith_short_12","alias_value":"OY575D4NXOSS","created_at":"2026-07-05T00:34:16.451804+00:00"},{"alias_kind":"pith_short_16","alias_value":"OY575D4NXOSSYFZL","created_at":"2026-07-05T00:34:16.451804+00:00"},{"alias_kind":"pith_short_8","alias_value":"OY575D4N","created_at":"2026-07-05T00:34:16.451804+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11083","citing_title":"Matrix element method at NLO: A fine proof of concept in POWHEG","ref_index":90,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14783","citing_title":"AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2507.02032","citing_title":"Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09495","citing_title":"Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY","json":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY.json","graph_json":"https://pith.science/api/pith-number/OY575D4NXOSSYFZLFEUQ64H7IY/graph.json","events_json":"https://pith.science/api/pith-number/OY575D4NXOSSYFZLFEUQ64H7IY/events.json","paper":"https://pith.science/paper/OY575D4N"},"agent_actions":{"view_html":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY","download_json":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY.json","view_paper":"https://pith.science/paper/OY575D4N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10621&json=true","fetch_graph":"https://pith.science/api/pith-number/OY575D4NXOSSYFZLFEUQ64H7IY/graph.json","fetch_events":"https://pith.science/api/pith-number/OY575D4NXOSSYFZLFEUQ64H7IY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY/action/storage_attestation","attest_author":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY/action/author_attestation","sign_citation":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY/action/citation_signature","submit_replication":"https://pith.science/pith/OY575D4NXOSSYFZLFEUQ64H7IY/action/replication_record"}},"created_at":"2026-07-05T00:34:16.451804+00:00","updated_at":"2026-07-05T00:34:16.451804+00:00"}