Machine Learning in KM3NeT

• Published in: EPJ Web of Conferences
• Main Author: De Sio Chiara
• Format: Article in Journal/Newspaper
• Language: English
• Published: EDP Sciences 2019
• Subjects: Physics; QC1-999; Orca
• Online Access: https://doi.org/10.1051/epjconf/201920705004; https://doaj.org/article/09818b2cf1ba4836af9cbee48b2db813

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.