{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WGSHJDEFJXPIHHQ5L2M2BHUTQL","short_pith_number":"pith:WGSHJDEF","schema_version":"1.0","canonical_sha256":"b1a4748c854dde839e1d5e99a09e9382d932b10d953f181d86053f9578c0eb4f","source":{"kind":"arxiv","id":"1910.12214","version":1},"attestation_state":"computed","paper":{"title":"Global Track Reconstruction and Data Compression Strategy in ALICE for LHC Run 3","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.ins-det","authors_text":"David Rohr (for the ALICE collaboration)","submitted_at":"2019-10-27T09:10:08Z","abstract_excerpt":"In LHC Run 3, ALICE will increase the data taking rate significantly, from an approximately 1 kHz trigger readout in minimum-bias Pb--Pb collisions to a 50 kHz continuous readout rate. The reconstruction strategy of the online-offline computing upgrade foresees a synchronous online reconstruction stage during data taking, which generates the detector calibration, and a posterior calibrated asynchronous reconstruction stage. The huge amount of data requires a significant compression in order to store all recorded events. The aim is a factor 20 compression of the TPC data, which is one of the ma"},"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":"1910.12214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ins-det","submitted_at":"2019-10-27T09:10:08Z","cross_cats_sorted":[],"title_canon_sha256":"3f4b024247181f5a166ad3216e6dd256383018000810ca38611e0a67b1eca39f","abstract_canon_sha256":"1b9f2b731b8b9fe8520d448bd93ecc0b2a142096d1e42db9b838973c04531d8e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:14.170038Z","signature_b64":"DbleBzXsye8DI+NPbz1V5wmhfnR1oz3ZPsbALred4hB4wjnP6lo1dV3HYmSfqmHEutbD/8UxgywvAywZTcXFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1a4748c854dde839e1d5e99a09e9382d932b10d953f181d86053f9578c0eb4f","last_reissued_at":"2026-07-05T00:15:14.169700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:14.169700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global Track Reconstruction and Data Compression Strategy in ALICE for LHC Run 3","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.ins-det","authors_text":"David Rohr (for the ALICE collaboration)","submitted_at":"2019-10-27T09:10:08Z","abstract_excerpt":"In LHC Run 3, ALICE will increase the data taking rate significantly, from an approximately 1 kHz trigger readout in minimum-bias Pb--Pb collisions to a 50 kHz continuous readout rate. The reconstruction strategy of the online-offline computing upgrade foresees a synchronous online reconstruction stage during data taking, which generates the detector calibration, and a posterior calibrated asynchronous reconstruction stage. The huge amount of data requires a significant compression in order to store all recorded events. The aim is a factor 20 compression of the TPC data, which is one of the ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.12214","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/1910.12214/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":"1910.12214","created_at":"2026-07-05T00:15:14.169756+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.12214v1","created_at":"2026-07-05T00:15:14.169756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.12214","created_at":"2026-07-05T00:15:14.169756+00:00"},{"alias_kind":"pith_short_12","alias_value":"WGSHJDEFJXPI","created_at":"2026-07-05T00:15:14.169756+00:00"},{"alias_kind":"pith_short_16","alias_value":"WGSHJDEFJXPIHHQ5","created_at":"2026-07-05T00:15:14.169756+00:00"},{"alias_kind":"pith_short_8","alias_value":"WGSHJDEF","created_at":"2026-07-05T00:15:14.169756+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09138","citing_title":"Usage of GPUs for online and offline Reconstruction in ALICE in Run 3","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL","json":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL.json","graph_json":"https://pith.science/api/pith-number/WGSHJDEFJXPIHHQ5L2M2BHUTQL/graph.json","events_json":"https://pith.science/api/pith-number/WGSHJDEFJXPIHHQ5L2M2BHUTQL/events.json","paper":"https://pith.science/paper/WGSHJDEF"},"agent_actions":{"view_html":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL","download_json":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL.json","view_paper":"https://pith.science/paper/WGSHJDEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.12214&json=true","fetch_graph":"https://pith.science/api/pith-number/WGSHJDEFJXPIHHQ5L2M2BHUTQL/graph.json","fetch_events":"https://pith.science/api/pith-number/WGSHJDEFJXPIHHQ5L2M2BHUTQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL/action/storage_attestation","attest_author":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL/action/author_attestation","sign_citation":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL/action/citation_signature","submit_replication":"https://pith.science/pith/WGSHJDEFJXPIHHQ5L2M2BHUTQL/action/replication_record"}},"created_at":"2026-07-05T00:15:14.169756+00:00","updated_at":"2026-07-05T00:15:14.169756+00:00"}