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arXiv Machine Learning By Felix J. Yu, Berthy T. Feng, Nicholas Kamp, Carlos A. Arg\"{u}elles

A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes

Read the original on arXiv Machine Learning →

The paper presents candela, a differentiable SIREN neural field that learns the photon Green's function for the IceCube Neutrino Observatory. It predicts photon yield and full arrival-time distribution for point-like energy deposits, enabling complete event simulation by superposing responses from multiple deposits. Trained on Monte‑Carlo data, candela produces events 50–100× faster than existing methods while maintaining median yields within 2% of MC expectations and timing distributions at the MC statistical floor across six photon‑count decades.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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