{
  "annotation_id": "2c10207c-c529-4bda-b8f1-887251b857d4",
  "date_created": "2026-03-02T18:00:59.805000Z",
  "date_modified": "2026-03-02T18:00:59.805000Z",
  "file_hash": "7b0581ce4937ad081ee4b3d51c548a9cb3c72ed5f6db8453f8fc2dbb9d7d9be9",
  "private": false,
  "record": {
    "abstract": "Modern analysis of high energy physics (HEP) data needs advanced statistical\ntools to separate signal from background. A C++ package has been implemented to\nprovide such tools for the HEP community. The package includes linear and\nquadratic discriminant analysis, decision trees, bump hunting (PRIM), boosting\n(AdaBoost), bagging and random forest algorithms, and interfaces to the\nstandard backpropagation neural net and radial basis function neural net\nimplemented in the Stuttgart Neural Network Simulator. Supplemental tools such\nas bootstrap, estimation of data moments, and a test of zero correlation\nbetween two variables with a joint elliptical distribution are also provided.\nThe package offers a convenient set of tools for imposing requirements on input\ndata and displaying output. Integrated in the BaBar computing environment, the\npackage maintains a minimal set of external dependencies and therefore can be\neasily adapted to any other environment. It has been tested on many idealistic\nand realistic examples.",
    "arxiv_id": "physics/0507143",
    "authors": [
      "I. Narsky"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "StatPatternRecognition: A C++ Package for Statistical Analysis of High   Energy Physics Data",
    "url": "https://arxiv.org/abs/physics/0507143"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "26592fcc-d4b3-4d82-983d-3347b4943fe5",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}