Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6
Résumé
KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal ofORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillationparameters. Additionally, the detector is also sensitive to a wide variety of phenomena includingnon-standard neutrino interactions, sterile neutrinos, and neutrino decay.This contribution describes the use of a machine learning framework for building Deep NeuralNetworks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based ona data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performanceof the model is evaluated by determining the sensitivity to oscillation parameters in comparisonwith the standard energy reconstruction method of maximizing a likelihood function. The resultsshow that the DNN is able to provide a better energy estimate with lower bias in the context ofoscillation analyses.