{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7QDZXEL3SHDWZ6XUE3RYQMFKUX","short_pith_number":"pith:7QDZXEL3","schema_version":"1.0","canonical_sha256":"fc079b917b91c76cfaf426e38830aaa5d0c35befe9d0c63ce1f3a0d8a45633a5","source":{"kind":"arxiv","id":"1812.09223","version":2},"attestation_state":"computed","paper":{"title":"Quark-Gluon Tagging: Machine Learning vs Detector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Gregor Kasieczka, Jennifer M. Thompson, Nicholas Kiefer, Tilman Plehn","submitted_at":"2018-12-21T16:07:12Z","abstract_excerpt":"Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists."},"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":"1812.09223","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2018-12-21T16:07:12Z","cross_cats_sorted":[],"title_canon_sha256":"bd5c06c8800d23c620ed9206945bbf25fe639854e9a712516d3cc71c01a8f7a0","abstract_canon_sha256":"01357b1e83acfb5507daeaf67b373a05c8b95d8b55f44b14502abb1b807b5564"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:03.982445Z","signature_b64":"0MuXXI+Hzz43GzjAzxvsF0Cqcf4ead+csqRP8KKDWNQllHbLtVWIkBFzHpslvt6gh8QuHLhyuw6dnj2dHJKqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc079b917b91c76cfaf426e38830aaa5d0c35befe9d0c63ce1f3a0d8a45633a5","last_reissued_at":"2026-05-17T23:43:03.982015Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:03.982015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quark-Gluon Tagging: Machine Learning vs Detector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Gregor Kasieczka, Jennifer M. Thompson, Nicholas Kiefer, Tilman Plehn","submitted_at":"2018-12-21T16:07:12Z","abstract_excerpt":"Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09223","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":""},"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":"1812.09223","created_at":"2026-05-17T23:43:03.982073+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.09223v2","created_at":"2026-05-17T23:43:03.982073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09223","created_at":"2026-05-17T23:43:03.982073+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QDZXEL3SHDW","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QDZXEL3SHDWZ6XU","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QDZXEL3","created_at":"2026-05-18T12:32:11.075285+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/7QDZXEL3SHDWZ6XUE3RYQMFKUX","json":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX.json","graph_json":"https://pith.science/api/pith-number/7QDZXEL3SHDWZ6XUE3RYQMFKUX/graph.json","events_json":"https://pith.science/api/pith-number/7QDZXEL3SHDWZ6XUE3RYQMFKUX/events.json","paper":"https://pith.science/paper/7QDZXEL3"},"agent_actions":{"view_html":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX","download_json":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX.json","view_paper":"https://pith.science/paper/7QDZXEL3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.09223&json=true","fetch_graph":"https://pith.science/api/pith-number/7QDZXEL3SHDWZ6XUE3RYQMFKUX/graph.json","fetch_events":"https://pith.science/api/pith-number/7QDZXEL3SHDWZ6XUE3RYQMFKUX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX/action/storage_attestation","attest_author":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX/action/author_attestation","sign_citation":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX/action/citation_signature","submit_replication":"https://pith.science/pith/7QDZXEL3SHDWZ6XUE3RYQMFKUX/action/replication_record"}},"created_at":"2026-05-17T23:43:03.982073+00:00","updated_at":"2026-05-17T23:43:03.982073+00:00"}