Event reconstruction for KM3NeT/ORCA using convolutional neural networks

Abstract

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.

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• Tau Neutrinos in the Next Decade: from GeV to EeV
• Inference of the Mass Composition of Cosmic Rays with energies from $10^{18.5}$ to $10^{20}$ eV using the Pierre Auger Observatory and Deep Learning
• A flexible event reconstruction based on machine learning and likelihood principles
• Measurement of the Depth of Maximum of Air-Shower Profiles with energies between $10^{18.5}$ and $10^{20}$ eV using the Surface Detector of the Pierre Auger Observatory and Deep Learning
• Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
• Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions
• Extending the Dark Matter Reach of Water Cherenkov Detectors using Jupiter
• Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory
• Using Machine Learning to Separate Cherenkov and Scintillation Light in Hybrid Neutrino Detector
• Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes
• Deep learning reconstruction in ANTARES
• Development of a trigger for acoustic neutrino candidates in KM3NeT
• Refine Neutrino Events Reconstruction with BEiT-3
• Neutrino Reconstruction in TRIDENT Based on Graph Neural Network