# A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes

arXiv: https://arxiv.org/abs/2609.04695 · HTML: https://mlsanity.com/paper/2609.04695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector](https://mlsanity.com/paper/2211.01505/) (2022)
- [End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors](https://mlsanity.com/paper/2602.24129/) (2026)
- [Running a Pre-Exascale, Geographically Distributed, Multi-Cloud Scientific Simulation](https://mlsanity.com/paper/2002.06667/) (2020)

## Abstract (arXiv)

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× 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.