Neutrino direction and flavor reconstruction from radio detector data using deep convolutional neural networks

Abstract

With the construction of RNO-G and plans for IceCube-Gen2, neutrino astronomy at EeV energies is at the horizon for the next years. Here, we determine the neutrino pointing capabilities and explore the sensitivity to the neutrino flavor for an array of shallow radio detector stations. The usage of deep learning for event reconstruction is enabled through recent advances in simulation codes that allow the simulation of realistic training data sets. A large dataset of expected radio signals for a broad range of neutrino energies between 100 PeV and 10 EeV is simulated using NuRadioMC. A deep neural network is trained on this low-level data and we find a direction resolution of a few degrees for all triggered events. We present the model architecture, how we optimized the model, and how robust the model is against systematic uncertainties. Furthermore, we explore the capabilities of a radio neutrino detector to determine the flavor id.