← Back to all news
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.

Related stories

arXiv AI
Oct 19

Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning

The paper presents three deep‑learning models— a gated spatiotemporal graph neural network and two Transformer‑based architectures— designed to identify and tag photons from $^{14}$C beta decay in large liquid scintillator detectors. By applying these models to simulated events containing both a $^{14}$C decay and a low‑energy positron, the authors achieve $^{14}$C recall rates between 25% and 48% while keeping positron misidentification below 1%. This selective tagging improves the total charge resolution for events where the $^{14}$C and positron photon hits overlap in space and time.

By Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li, Wuming Luo, Jiahui Wei, Wenxing Fang, Hehe Fan
llms
More like this →
arXiv Machine Learning
Aug 27

Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

The paper applies sparse autoencoders to a neutrino foundation model trained on IceCube data, uncovering a validated atlas of physical concepts within the model’s internal representation. Causal analysis shows the direction reconstruction head largely ignores this atlas, whereas an uncertainty head trained on the same representation effectively uses quality and brightness features, improving angular resolution from 20.2° to 3.2° at 20% efficiency. These findings demonstrate that mechanistic interpretability can expose latent physics and guide the design of downstream tasks.

By Rapha\"el Bonnet-Guerrini, Johann Ioannou-Nikolaides, Inar Timiryasov, Vincenzo Piuri
fine-tuningsafety
More like this →
arXiv Machine Learning
Oct 16

Machine-learning-based neutrino-event generation constrained by experimental measurements

arXiv:2610. 20870v1 Announce Type: cross Abstract: We present a generative surrogate for charged-current \(\nu_\mu\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples.

By Jose Luis Bonilla, Krzysztof M. Graczyk, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk
fine-tuning
More like this →