Speaker
Description
We present an end-to-end reconstruction of the neutrino energy, direction and flavor from shallow in-ice radio detector data using deep neural networks (DNNs). For the first time, we were able to determine the neutrino direction with a few degrees resolution also for the complicated event class of electron neutrino charged-current interactions where the shower development is impacted by the LPM effect. This result highlights the advantages of DNNs to model the complex correlations in radio detector data. We will present an outlook of extending the model to predict the complex probability distribution of the neutrino direction using Normalizing Flows. Furthermore, we discuss how this work can be used for real-time alerts and an end-to-end detector optimization of, e.g., IceCube-Gen2 radio.