{
  "annotation_id": "254d76d0-c061-4c52-ae1d-8f59c897cdf9",
  "date_created": "2026-03-02T18:00:43.472000Z",
  "date_modified": "2026-03-02T18:00:43.472000Z",
  "file_hash": "df075effd598a3109c7f9a9efec78c07fdb52bf18714d6a9459c7dc7246173be",
  "private": false,
  "record": {
    "abstract": "A single and self-contained method for determining the crystallite-size\ndistribution and shape from experimental x-ray line profile data is presented.\nWe have shown that the crystallite-size distribution can be determined without\nassuming a functional form for the size distribution, determining instead the\nsize distribution with the least assumptions by applying the Bayesian/MaxEnt\nmethod. The Bayesian/MaxEnt method is tested using both simulated and\nexperimental CeO$_{2}$ data. The results demonstrate that the proposed method\ncan determine size distributions, while making the least number of assumptions.\nThe comparison of the Bayesian/MaxEnt results from experimental CeO$_2$ with\nTEM results is favorable",
    "arxiv_id": "physics/0305018",
    "authors": [
      "N. Armstrong",
      "W. Kalceff",
      "J. P. Cline",
      "J. Bonevich"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "Bayesian inference of nanoparticle-broadened x-ray line profiles",
    "url": "https://arxiv.org/abs/physics/0305018"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c3fe5c0c-fa95-4566-ad14-82bfaf5415f6",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}