Volume 444 - 38th International Cosmic Ray Conference (ICRC2023) - Neutrino Astronomy & Physics (NU)
2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore
J.H. Peterson*, M. Prado Rodriguez, K. Hanson  on behalf of the IceCube Collaboration, R. Abbasi, M. Ackermann, et al. (click to show)
*: corresponding author
Full text: pdf
Pre-published on: August 08, 2023
Published on: September 27, 2024
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.
DOI: https://doi.org/10.22323/1.444.1129
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