Event Reconstruction for KM3NeT/ORCA Using Convolutional Neural Networks

The KM3NeT research infrastructure is currently under construction at twolocations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrinodetector off the French coast will instrument several megatons of seawater withphotosensors. Its main objective is the determination of the neutrino massordering. This work aims at demonstrating the general applicability of deepconvolutional neural networks to neutrino telescopes, using simulated datasetsfor the KM3NeT/ORCA detector as an example. To this end, the networks areemployed to achieve reconstruction and classification tasks that constitute analternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeTLetter of Intent. They are used to infer event reconstruction estimates for theenergy, the direction, and the interaction point of incident neutrinos. Thespatial distribution of Cherenkov light generated by charged particles inducedin neutrino interactions is classified as shower- or track-like, and the mainbackground processes associated with the detection of atmospheric neutrinos arerecognized. Performance comparisons to machine-learning classification andmaximum-likelihood reconstruction algorithms previously developed forKM3NeT/ORCA are provided. It is shown that this application of deepconvolutional neural networks to simulated datasets for a large-volume neutrinotelescope yields competitive reconstruction results and performanceimprovements with respect to classical approaches.