{
  "abstract": "Large-volume neutrino telescopes infer neutrino properties from Cherenkov light, but simulating the transport of billions of photons through highly scattering ice or water is computationally costly. We introduce candela, a differentiable SIREN neural field that learns the photon Green's function of the IceCube Neutrino Observatory, a cubic-kilometer detector embedded in Antarctic glacial ice. Given a point-like energy deposit and sensor, it predicts the expected photon yield and full arrival-time distribution at the sensor. Complete events are simulated by decomposing charged-particle energy deposits into point-like sources and superposing their predicted sensor responses. Trained on Monte-Carlo simulations, candela generates events $50$--$100\\times$ faster than existing methods, with cost scaling only weakly with neutrino energy. It keeps median yields within $2\\%$ of the MC expectation and timing distributions at the MC statistical floor across six photon-count decades. The model also provides end-to-end gradients with respect to event parameters and opens a path toward optimizing scattering-medium properties, which often dominate systematic uncertainties in neutrino telescopes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04695",
  "author_total": 4,
  "authors": [
    {
      "name": "Felix J. Yu"
    },
    {
      "name": "Berthy T. Feng"
    },
    {
      "name": "Nicholas Kamp"
    },
    {
      "name": "Carlos A. Argüelles"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9709wvz05v6vq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04695",
  "fulltext_url": null,
  "id": "9709wvz05v6vq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04695",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "openalex",
      "3106|Neutrino Physics Research"
    ]
  ],
  "openalex": "W7211896129",
  "pdf_url": "https://arxiv.org/pdf/2609.04695",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9709wvz05v6vq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:14Z",
      "native_id": "2609.04695",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04695"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes",
  "title_text": "A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:14Z",
  "url": "https://research.draconitic.com/paper/9709wvz05v6vq",
  "venue": "arXiv",
  "versions": []
}