{
  "annotation_id": "3ab96351-9906-4a67-be66-09e17117f584",
  "date_created": "2026-03-02T18:00:43.060000Z",
  "date_modified": "2026-03-02T18:00:43.060000Z",
  "file_hash": "f32f32a36b99103aa199e9505785e764ce33bf3dfe19844f24b2b069dd7e286b",
  "private": false,
  "record": {
    "abstract": "Track finding and fitting algorithm in the ALICE Time projection chamber\n(TPC) based on Kalman-filtering is presented. Implementation of particle\nidentification (PID) using d$E$/d$x$ measurement is discussed. Filtering and\nPID algorithm is able to cope with non-Gaussian noise as well as with ambiguous\nmeasurements in a high-density environment. The occupancy can reach up to 40%\nand due to the overlaps, often the points along the track are lost and others\nare significantly displaced. In the present algorithm, first, clusters are\nfound and the space points are reconstructed. The shape of a cluster provides\ninformation about overlap factor. Fast spline unfolding algorithm is applied\nfor points with distorted shapes. Then, the expected space point error is\nestimated using information about the cluster shape and track parameters.\nFurthermore, available information about local track overlap is used. Tests are\nperformed on simulation data sets to validate the analysis and to gain\npractical experience with the algorithm.",
    "arxiv_id": "physics/0306108",
    "authors": [
      "M. Ivanov",
      "K. Safarik",
      "Y. Belikov",
      "J. Bracinik"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "TPC tracking and particle identification in high-density environment",
    "url": "https://arxiv.org/abs/physics/0306108"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9d934da8-db55-4bcb-99f1-a2f0def27805",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}