Event reconstruction for KM3NeT/ORCA using convolutional neural networks

• Published in: Journal of Instrumentation
• Main Authors: Aiello, S., Böttcher, M., Kreter, M., Zywucka, N.
• Other Authors: 24420530 - Böttcher, Markus, 33379009 - Kreter, Michael, 34208968 - Zywucka-Hejzner, Natalia
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2020
• Subjects: Cherenkov detectors; Large detector systems for particle and astroparticle physics; Neutrino detectors; Performance of High Energy Physics Detectors; Orca
• Online Access: http://hdl.handle.net/10394/36109; https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005/pdf; https://doi.org/10.1088/1748-0221/15/10/P10005

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