{"paper":{"title":"A Novel Segment-Based Tracking Algorithm for HLT under High-Occupancy and Complex Conditions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A segment-based algorithm with 11 pre-defined patterns reduces global tracking elements to 400-500 at 25% occupancy in gaseous detectors.","cross_cats":["hep-ex"],"primary_cat":"physics.ins-det","authors_text":"Changqing Feng, Hang Zhou, Jianbei Liu, Pengkun Jia, Yuhe Huang, Zhujun Fang","submitted_at":"2026-05-15T03:37:04Z","abstract_excerpt":"In the High-Level Trigger (HLT) of both electron-positron and hadron collision experiments, the tracking process for large-volume gaseous detectors typically consumes a latency of hundreds of milliseconds. Upgrades of existing experiments and the development of next-generation facilities demand enhanced HLT tracking performance: handling higher detector occupancy and suppressing latency. To address high occupancy conditions, a novel HLT tracking algorithm based on track segments is proposed. This method involves constructing a pattern bank comprising 11 pre-defined patterns, optimizing edge-ma"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"With the depth-first search within connected components, the simulation results show that the algorithm maintains stable performance with occupancy ranging from 5% to 25%, achieving a data compression ratio of approximately 50% to 70%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The 11 pre-defined patterns together with position-momentum-timing criteria for edge-matrix formation and stereo superlayer merging are sufficient to capture relevant tracks without significant efficiency loss under high-occupancy conditions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new segment-based HLT tracking algorithm using 11 patterns and optimized edge matrices reduces global tracking complexity to 400-500 elements at 25% occupancy with 50-70% data compression.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A segment-based algorithm with 11 pre-defined patterns reduces global tracking elements to 400-500 at 25% occupancy in gaseous detectors.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f0fcfbdcfc653a1e6cde44d71add64810e2c2b0b851a64008dc94d8183a29fbb"},"source":{"id":"2605.15577","kind":"arxiv","version":1},"verdict":{"id":"4c3fb045-f10a-470a-9055-70bcd6d81e14","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:55:18.160948Z","strongest_claim":"With the depth-first search within connected components, the simulation results show that the algorithm maintains stable performance with occupancy ranging from 5% to 25%, achieving a data compression ratio of approximately 50% to 70%.","one_line_summary":"A new segment-based HLT tracking algorithm using 11 patterns and optimized edge matrices reduces global tracking complexity to 400-500 elements at 25% occupancy with 50-70% data compression.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The 11 pre-defined patterns together with position-momentum-timing criteria for edge-matrix formation and stereo superlayer merging are sufficient to capture relevant tracks without significant efficiency loss under high-occupancy conditions.","pith_extraction_headline":"A segment-based algorithm with 11 pre-defined patterns reduces global tracking elements to 400-500 at 25% occupancy in gaseous detectors."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15577/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T20:01:43.318420Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.295818Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:34:35.250227Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:41:56.072937Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"b75b4c78f193b442fcb12ea5be5eee7951550fb64d2aae9f1aa8425745aed2bc"},"references":{"count":21,"sample":[{"doi":"","year":2000,"title":"The large hadron collider–present status and prospects,","work_id":"16914d45-abd8-4534-815d-33b9f77a9e4d","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"ATLAS event selection system readies for LHC Run 3,","work_id":"da8fa97e-868a-4308-8dce-1c98453faa1f","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"A vailable: https://atlas.cern/updates/briefing/ run-3-trigger","work_id":"639910c3-0e10-4b02-9860-991ed29ddb92","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Performance of the cms high-level trigger during LHC Run 2,","work_id":"b0984c5e-291a-4fd4-a278-e83907139ac0","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Performance of Belle II tracking on collision data,","work_id":"9e181529-432b-41b1-a1ef-8874f71301e8","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":21,"snapshot_sha256":"a13daef61134d81d3d27721b4b44476b4237bc845dde053cab6dbe248555a61d","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"395512b7828e5a3efcf2039de13dd1516bec361f29ad0d88add811421475bfe3"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}