{
  "annotation_id": "0b8de123-d717-46c2-bdb8-baa8486e9928",
  "date_created": "2026-03-02T18:00:46.671000Z",
  "date_modified": "2026-03-02T18:00:46.671000Z",
  "file_hash": "1f15956b0117d231a0c0e861f272e7d00b8dd37eceed2bc7d8c1701a4503f785",
  "private": false,
  "record": {
    "abstract": "A new method for calculation of goodness of multidimensional fits in particle\nphysics experiments is proposed. This method finds the smallest and largest\nclusters of nearest neighbors for observed data points. The cluster size is\nused to estimate the goodness-of-fit and the cluster location provides clues\nabout possible problems with data modeling. The performance of the new method\nis compared to that of the likelihood method and Kolmogorov-Smirnov test using\ntoy Monte Carlo studies. The new method is applied to estimate the\ngoodness-of-fit in a B-\u003eKll analysis at BaBar.",
    "arxiv_id": "physics/0306171",
    "authors": [
      "Ilya Narsky"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "Estimation of Goodness-of-Fit in Multidimensional Analysis Using   Distance to Nearest Neighbor",
    "url": "https://arxiv.org/abs/physics/0306171"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3d697588-9ba2-43b6-8bc3-ed402cd53177",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}