{
  "annotation_id": "d91611dd-b67e-43b7-b0cc-4efc857252cd",
  "date_created": "2026-02-17T05:53:08.713000Z",
  "date_modified": "2026-02-17T05:53:08.713000Z",
  "file_hash": "bdff3a0c3286a150135950eec7b7a31732101ec9c4233f3995962d465a913fa3",
  "private": false,
  "record": {
    "abstract": "The preparation of next-generation large-scale radio experiments requires running a fast and efficient number of simulations to explore multiple detector configurations over vast areas and develop novel methods for the reconstruction of air shower parameters. While Monte Carlo simulations are accurate and reliable tools, they are too computationally expensive to explore the full parameter space of these new detectors within a reasonable timescale. We introduce a new version of Radio Morphing, a semi-analytical tool designed to simulate the radio emission of any cosmic-ray induced air shower with zenith angle $\\theta\u003e60^{\\circ}$, at any desired antenna position, from the simulation data of a few reference showers at given positions. We present the latest performances of Radio Morphing which now provides simulation of air shower radio signals with average relative differences on the peak amplitude below $17\\%$ on raw traces, below $15\\%$ with a $3\\sigma$ trigger threshold, below $13\\%$ in the $[50-200]\\,\\rm MHz$ band, and even below $\\sim 10\\%$ in the $[30-80]\\,\\rm MHz$ band. These results are combined with a computation time reduced by more than four orders of magnitude, compared to standard Monte Carlo simulations.",
    "arxiv_id": "2601.06418",
    "authors": [
      "Simon Chiche",
      "Olivier Martineau-Huynh",
      "Matias Tueros",
      "Krijn D. de Vries"
    ],
    "categories": [
      "astro-ph.HE",
      "astro-ph.IM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Radio Morphing: Fast computation of inclined air shower radio emission",
    "url": "https://arxiv.org/abs/2601.06418",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c8b94d74-8e56-4aa3-bb99-1f6bf19eb07c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}