A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes

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.

9 pages, 5 figures. Submitted to the Sim2Sci Workshop @ NeurIPS 2026

High Energy Astrophysical Phenomena (astro-ph.HE); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)

arXiv:2609.04695 [astro-ph.HE]

(or arXiv:2609.04695v1 [astro-ph.HE] for this version)

https://doi.org/10.48550/arXiv.2609.04695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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