{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3BIJEAMTNYODSYLHQVRFWNAW5A","short_pith_number":"pith:3BIJEAMT","schema_version":"1.0","canonical_sha256":"d8509201936e1c39616785625b3416e802612047686290d596681d03b4f4fb60","source":{"kind":"arxiv","id":"2211.01421","version":4},"attestation_state":"computed","paper":{"title":"Modern Machine Learning for LHC Physicists","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Anja Butter, Barry Dillon, Claudius Krause, Ramon Winterhalder, Theo Heimel, Tilman Plehn","submitted_at":"2022-11-02T18:27:27Z","abstract_excerpt":"Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction t"},"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":"2211.01421","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2022-11-02T18:27:27Z","cross_cats_sorted":[],"title_canon_sha256":"62e553f5b84883b5bdbfd79eefb4eea6222d74b8cc2180e3b1516393471eeb83","abstract_canon_sha256":"943bd1497960634c36699fa6f59be6e941d34585c41cd986302f944f70627dca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:07.403892Z","signature_b64":"wMz1OTAoIv/bWL3DAxQVt+HaqX8RykSaFzvmEr76VigiqYZCcs9FSff6UH38ZV5bp6VVHxjrymBff9lpWpgcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8509201936e1c39616785625b3416e802612047686290d596681d03b4f4fb60","last_reissued_at":"2026-07-05T10:53:07.403399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:07.403399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modern Machine Learning for LHC Physicists","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Anja Butter, Barry Dillon, Claudius Krause, Ramon Winterhalder, Theo Heimel, Tilman Plehn","submitted_at":"2022-11-02T18:27:27Z","abstract_excerpt":"Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.01421","kind":"arxiv","version":4},"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/2211.01421/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":"2211.01421","created_at":"2026-07-05T10:53:07.403456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.01421v4","created_at":"2026-07-05T10:53:07.403456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.01421","created_at":"2026-07-05T10:53:07.403456+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BIJEAMTNYOD","created_at":"2026-07-05T10:53:07.403456+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BIJEAMTNYODSYLH","created_at":"2026-07-05T10:53:07.403456+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BIJEAMT","created_at":"2026-07-05T10:53:07.403456+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01354","citing_title":"Local Conformal Predictions for Calibrated Surrogates","ref_index":104,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31214","citing_title":"EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18360","citing_title":"Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17318","citing_title":"RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18360","citing_title":"Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2506.21433","citing_title":"Probing Neutral Triple Gauge Couplings via $ZZ$ Production at $e^+e^-$ Colliders with Machine Learning","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2509.01677","citing_title":"Machine Learning in the 2HDM2S model for Dark Matter","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2509.00155","citing_title":"Amplitude Uncertainties Everywhere All at Once","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07470","citing_title":"Uncovering Hidden Systematics in Neural Network Models for High Energy Physics","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A","json":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A.json","graph_json":"https://pith.science/api/pith-number/3BIJEAMTNYODSYLHQVRFWNAW5A/graph.json","events_json":"https://pith.science/api/pith-number/3BIJEAMTNYODSYLHQVRFWNAW5A/events.json","paper":"https://pith.science/paper/3BIJEAMT"},"agent_actions":{"view_html":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A","download_json":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A.json","view_paper":"https://pith.science/paper/3BIJEAMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.01421&json=true","fetch_graph":"https://pith.science/api/pith-number/3BIJEAMTNYODSYLHQVRFWNAW5A/graph.json","fetch_events":"https://pith.science/api/pith-number/3BIJEAMTNYODSYLHQVRFWNAW5A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A/action/storage_attestation","attest_author":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A/action/author_attestation","sign_citation":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A/action/citation_signature","submit_replication":"https://pith.science/pith/3BIJEAMTNYODSYLHQVRFWNAW5A/action/replication_record"}},"created_at":"2026-07-05T10:53:07.403456+00:00","updated_at":"2026-07-05T10:53:07.403456+00:00"}