{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:Y7BN3QUJZOLMMDRYOB5MM54G7T","short_pith_number":"pith:Y7BN3QUJ","schema_version":"1.0","canonical_sha256":"c7c2ddc289cb96c60e38707ac67786fcc6eb4be25c91803a7216288aea253b08","source":{"kind":"arxiv","id":"2209.07559","version":1},"attestation_state":"computed","paper":{"title":"Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","hep-ex","hep-lat","hep-th"],"primary_cat":"physics.comp-ph","authors_text":"Daniel Whiteson, Kazuhiro Terao, Phiala Shanahan","submitted_at":"2022-09-15T18:46:48Z","abstract_excerpt":"The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring alloc"},"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":"2209.07559","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2022-09-15T18:46:48Z","cross_cats_sorted":["cs.AI","hep-ex","hep-lat","hep-th"],"title_canon_sha256":"b36ac1b70c842ec7fafd8861d1bfa794ecb11db83c543ff01fe47c711b6d82af","abstract_canon_sha256":"9df7474eafc36465ef239ffedcf7007e2195024b62e80374f8809e5b9a477cef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:07.211733Z","signature_b64":"2FlTRGulnH8GOeVdw+n1Rl5sTmKUWnaK5UJy7h7iRur6IhMrnPeA46ltENjEcZNP+8YM6gC1sggTl8gB5/UsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7c2ddc289cb96c60e38707ac67786fcc6eb4be25c91803a7216288aea253b08","last_reissued_at":"2026-07-05T04:58:07.211266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:07.211266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","hep-ex","hep-lat","hep-th"],"primary_cat":"physics.comp-ph","authors_text":"Daniel Whiteson, Kazuhiro Terao, Phiala Shanahan","submitted_at":"2022-09-15T18:46:48Z","abstract_excerpt":"The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring alloc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.07559","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/2209.07559/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":"2209.07559","created_at":"2026-07-05T04:58:07.211323+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.07559v1","created_at":"2026-07-05T04:58:07.211323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.07559","created_at":"2026-07-05T04:58:07.211323+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y7BN3QUJZOLM","created_at":"2026-07-05T04:58:07.211323+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y7BN3QUJZOLMMDRY","created_at":"2026-07-05T04:58:07.211323+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y7BN3QUJ","created_at":"2026-07-05T04:58:07.211323+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01354","citing_title":"Local Conformal Predictions for Calibrated Surrogates","ref_index":105,"is_internal_anchor":false},{"citing_arxiv_id":"2601.16391","citing_title":"Extraction of the color dipole amplitude with physics-informed neural networks","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2603.28604","citing_title":"Hadron Structure from lattice QCD in the context of the Electron-Ion Collider","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02906","citing_title":"Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22462","citing_title":"Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective","ref_index":218,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T","json":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T.json","graph_json":"https://pith.science/api/pith-number/Y7BN3QUJZOLMMDRYOB5MM54G7T/graph.json","events_json":"https://pith.science/api/pith-number/Y7BN3QUJZOLMMDRYOB5MM54G7T/events.json","paper":"https://pith.science/paper/Y7BN3QUJ"},"agent_actions":{"view_html":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T","download_json":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T.json","view_paper":"https://pith.science/paper/Y7BN3QUJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.07559&json=true","fetch_graph":"https://pith.science/api/pith-number/Y7BN3QUJZOLMMDRYOB5MM54G7T/graph.json","fetch_events":"https://pith.science/api/pith-number/Y7BN3QUJZOLMMDRYOB5MM54G7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T/action/storage_attestation","attest_author":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T/action/author_attestation","sign_citation":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T/action/citation_signature","submit_replication":"https://pith.science/pith/Y7BN3QUJZOLMMDRYOB5MM54G7T/action/replication_record"}},"created_at":"2026-07-05T04:58:07.211323+00:00","updated_at":"2026-07-05T04:58:07.211323+00:00"}