2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore

The IceCube Collaboration

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Article number1129
JournalProceedings of Science
Volume444
StatePublished - Sep 27 2024
Event38th International Cosmic Ray Conference, ICRC 2023 - Nagoya, Japan
Duration: Jul 26 2023Aug 3 2023

All Science Journal Classification (ASJC) codes

  • General

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