# Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution

arXiv: https://arxiv.org/abs/2408.08474 · HTML: https://mlsanity.com/paper/2408.08474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [A Convolutional Neural Network Neutrino Event Classifier](https://mlsanity.com/paper/1604.01444/) (2016)
- [Graph Neural Networks for IceCube Signal Classification](https://mlsanity.com/paper/1809.06166/) (2018)
- [Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data](https://mlsanity.com/paper/1903.05663/) (2019)
- [Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors](https://mlsanity.com/paper/2102.01033/) (2021)
- [A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber](https://mlsanity.com/paper/1808.07269/) (2018)
- [Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube](https://mlsanity.com/paper/2107.12110/) (2021)
- [Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks](https://mlsanity.com/paper/2303.08812/) (2023)
- [Observation of high-energy neutrinos from the Galactic plane](https://mlsanity.com/paper/2307.04427/) (2023)
- [KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen](https://mlsanity.com/paper/2203.01870/) (2022)

## Abstract (arXiv)

Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing (10-100,rm m) between them. In this letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep learning-driven super-resolution of data events. These ``improved'' events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional ``virtual'' optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies.