{
  "annotation_id": "cf739f78-dc7f-4b2c-afe1-dc8afa3a5ce6",
  "date_created": "2026-03-02T18:01:25.421000Z",
  "date_modified": "2026-03-02T18:01:25.421000Z",
  "file_hash": "04be8f42a5b5810a2afb1f2dd7186dd2dd9da85a8d07c139e311d3e1a0ea1826",
  "private": false,
  "record": {
    "abstract": "The interpretation of new particle search results involves a confidence level\ncalculation on either the discovery hypothesis or the background-only (\"null\")\nhypothesis. A typical approach uses toy Monte Carlo experiments to build an\nexpected experiment estimator distribution against which an observed\nexperiment\u0027s estimator may be compared. In this note, a new approach is\npresented which calculates analytically the experiment estimator distribution\nvia a Fourier transform, using the likelihood ratio as an ordering estimator.\nThe analytic approach enjoys an enormous speed advantage over the toy Monte\nCarlo method, making it possible to quickly and precisely calculate confidence\nlevel results.",
    "arxiv_id": "physics/9906010",
    "authors": [
      "Hongbo Hu",
      "Jason Nielsen"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "Analytic Confidence Level Calculations using the Likelihood Ratio and   Fourier Transform",
    "url": "https://arxiv.org/abs/physics/9906010"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "abd414f3-8261-496c-ae21-ea4d7b78938b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}