{
  "annotation_id": "34fcfaaf-3402-4fbd-9964-bcc5d55e6b9e",
  "date_created": "2026-03-02T18:00:49.943000Z",
  "date_modified": "2026-03-02T18:00:49.943000Z",
  "file_hash": "8bc457454c797f4c73358730a36bf4dbb2caf93bdf4f586399973561901439a9",
  "private": false,
  "record": {
    "abstract": "The central detectors of the ALICE experiment at LHC will produce a data size\nof up to 75 MByte/event at an event rate \u003c200 Hz resulting in a data rate of\n\\~15 GByte/sec. This exceeds the foreseen mass storage bandwidth of 1.25\nGByte/sec by one order of magnitude. Online processing of the data is necessary\nin order to select interesting (sub)events (\"High Level Trigger\"), or to\ncompress data efficiently by modeling techniques.\n  The largest computing challenge is imposed by the TPC, requiring realtime\npattern recognition. The work carried out investigates the performance of\nvarious pattern recognition schemes applicable for online track reconstruction\nwithin the ALICE HLT system. Furthermore, as an application for reducing the\ndata rate, very efficient TPC data compression algorithms based on track and\ncluster modeling have been evaluated.",
    "arxiv_id": "physics/0406003",
    "authors": [
      "Anders Vestbo"
    ],
    "categories": [
      "physics.ins-det"
    ],
    "title": "Pattern Recognition and Data Compression for the ALICE High Level   Trigger",
    "url": "https://arxiv.org/abs/physics/0406003"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "35263c4d-147c-4bc7-8ed7-846cd2b224e0",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}