{
  "annotation_id": "42231d39-ad7f-4c3e-8cf2-4d4b1384c041",
  "date_created": "2026-03-02T18:00:46.501000Z",
  "date_modified": "2026-03-02T18:00:46.501000Z",
  "file_hash": "0608e061575378e384fc9c0fcc20e0b9649fee62802d2765193e5ce23942ae06",
  "private": false,
  "record": {
    "abstract": "The ALICE High Level Trigger has to process data online, in order to select\ninteresting (sub)events, or to compress data efficiently by modeling\ntechniques.Focusing on the main data source, the Time Projection Chamber (TPC),\nwe present two pattern recognition methods under investigation: a sequential\napproach \"cluster finder\" and \"track follower\") and an iterative approach\n(\"track candidate finder\" and \"cluster deconvoluter\"). We show, that the former\nis suited for pp and low multiplicity PbPb collisions, whereas the latter might\nbe applicable for high multiplicity PbPb collisions, if it turns out, that more\nthan 8000 charged particles would have to be reconstructed inside the TPC.\nBased on the developed tracking schemes we show, that using modeling techniques\na compression factor of around 10 might be achievable",
    "arxiv_id": "physics/0307102",
    "authors": [
      "V. Lindenstruth",
      "C. Loizides",
      "D. Roehrich",
      "B. Skaali",
      "T. Steinbeck",
      "R. Stock",
      "H. Tilsner",
      "K. Ullaland",
      "A. Vestbo",
      "T. Vik"
    ],
    "categories": [
      "physics.ins-det"
    ],
    "doi": "10.1109/TNS.2004.829384",
    "title": "Online Pattern Recognition for the ALICE High Level Trigger",
    "url": "https://arxiv.org/abs/physics/0307102"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ba3c2b62-3bb6-4309-9a9b-c644e0a47814",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}