2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore
Abstract
IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction.
Keywords
Cite
@article{arxiv.2307.16373,
title = {2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore},
author = {J. H. Peterson and M. Prado Rodriguez and K. Hanson},
journal= {arXiv preprint arXiv:2307.16373},
year = {2026}
}
Comments
Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions