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.

翻译：IceCube DeepCore是冰立方中微子天文台的扩展部分，专为测量GeV尺度大气中微子相互作用而设计，用于中微子振荡研究。由于探测器稀疏的仪器布局，在IceCube DeepCore中区分缪子中微子与其他味型的中微子以及重建非弹性度，在GeV尺度能量下尤为困难。卷积神经网络（CNN）已被发现比传统基于似然的方法在中微子事件重建方面具有更优表现。本文提出一种新型CNN模型，该模型利用了IceCube DeepCore数据中的时间和深度平移对称性，并展示了模型在味型识别和非弹性度重建方面的性能。