{
  "annotation_id": "eeae8860-2f18-48ff-beda-fe4d49e773de",
  "date_created": "2026-03-02T18:01:14.976000Z",
  "date_modified": "2026-03-02T18:01:14.976000Z",
  "file_hash": "8af86e019f524a94a9bbdc395270a36dd257fc98225621c56494787a38e77920",
  "private": false,
  "record": {
    "abstract": "The cascade training technique which was developed during our work on the\nMiniBooNE particle identification has been found to be a very efficient way to\nimprove the selection performance, especially when very low background\ncontamination levels are desired. The detailed description of this technique is\npresented here based on the MiniBooNE detector Monte Carlo simulations, using\nboth artifical neural networks and boosted decision trees as examples.",
    "arxiv_id": "physics/0611267",
    "authors": [
      "Yong Liu",
      "Ion Stancu"
    ],
    "categories": [
      "physics.data-an",
      "hep-ex"
    ],
    "doi": "10.1016/j.nima.2007.05.173",
    "journal_ref": "Nucl.Instrum.Meth.A578:315-321,2007",
    "title": "Cascade Training Technique for Particle Identification",
    "url": "https://arxiv.org/abs/physics/0611267"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "eeb56a51-91cd-4cd3-85b2-ceaac566e70a",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}