{
  "annotation_id": "a0cdcd45-8fb5-4442-8279-dfb0f576f56b",
  "date_created": "2026-03-02T18:00:42.801000Z",
  "date_modified": "2026-03-02T18:00:42.801000Z",
  "file_hash": "a5a83b6f27ea6092ac8314040da9a554cf883649308d4b9e8f2fb8bbb8da74e1",
  "private": false,
  "record": {
    "abstract": "To maximize the accuracy of background simulation and event reconstruction,\nhigh-energy neutrino telescopes require detailed knowledge of light propagation\nover a large volume of detection medium. If light scattering and absorption\nleng ths in the medium are of the same scale as the detector size, this problem\ncan only be handled numerically. Any inhomogeneity of optical properties in the\nmedium further complicates the problem, requiring large computational\nresources. We present a treatment based on combining ray-tracing Monte Carlo\nand neural network techniques which offers a reasonable compromise between\nsolution accuracy, computer memory and CPU usage.",
    "arxiv_id": "physics/0305097",
    "authors": [
      "Predrag Miocinovic",
      "Peter Niessen"
    ],
    "categories": [
      "physics.comp-ph"
    ],
    "title": "Computational Techniques for Simulating Light Propagation in High-Energy   Neutrino Telescopes",
    "url": "https://arxiv.org/abs/physics/0305097"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "447cdd3b-bc81-4f5b-b6e4-974bc35878c7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}