Vertex and energy reconstruction in JUNO with machine learning methods

The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters sin2θ12, Δm212 and Δm312 are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present several machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. The models of BDT and DNN are trained with aggregated information, pre-calculated from PMT signal, while the others are trained with PMT-wise measured information from 17600 PMTs. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: σE=3% at Evis=1MeV for the energy and σx,y,z=10cm at Evis=1MeV for the position.