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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an early-stage machine learning surrogate model (candela) designed to accelerate photon propagation simulation for the IceCube neutrino detector. Benchmarked against Monte Carlo simulations, it achieves 50–100× speedup with median yield accuracy within 2% and timing distributions at the statistical floor of the reference, but lacks peer review and validation against real detector data.
Uncontrolled computational method development and benchmarking against Monte Carlo reference. IceCube Neutrino Observatory cubic-kilometer detector in Antarctic glacial ice; simulated neutrino events.. Intervention: candela neural field model for photon propagation prediction.. Compared with: Standard Monte Carlo photon transport simulation methods.. IceCube detector, Antarctica (training data from simulation only; no field validation reported)..
candela generates events 50–100× faster than existing methods Median photon yields kept within 2% of Monte Carlo expectation Timing distributions match Monte Carlo statistical floor across six photon-count decades
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Not applicable; this is a computational physics methods paper. For neutrino physics researchers: if validated externally, this could accelerate event reconstruction and systematic uncertainty quantification, but real-detector validation is essential before operational deployment.
A proof-of-concept computational method demonstrating feasibility and performance metrics against simulation benchmarks, but lacking validation against real detector data, peer review, or demonstration of clinical/scientific decision impact.
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Quoted from the source exactly as published.
Not applicable; this is a computational physics methods paper. For neutrino physics researchers: if validated externally, this could accelerate event reconstruction and systematic uncertainty quantification, but real-detector validation is essential before operational deployment.
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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.
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