Deep Neural Networks per la ricostruzione dell’energia di eventi di decadimento beta inverso nell’esperimento JUNO

Modern experiments on the intensity frontier requires complicate algorithms to extract data for physical analysis. The neural network techniques, having received a considerable boost during the last years, are becoming a useful tool for addressing many tasks of data processing and provide in some cases better performance than traditional methods. The Jiangmen Underground Neutrino Observatory (JUNO), a next generation experiment under construction in South of China, has been designed to measure the neutrino mass hierarchy. Moreover, thanks to its large active mass, JUNO will be able to observe neutrinos coming from different sources: solar neutrinos, atmospheric neutrinos, geo-neutrinos and neutrinos produced by the explosion of supernovae. The core of the experiment is made of 20 kton Liquid Scintillator whose scintillation light is seen by almost 20000 large size (20") photo-multipliers with high quantum efficiency, and by 25000 small size (3") photo-multipliers. The raw data will have to be further processed to reconstruct the proper observables and for this task deep neural networks will be adopted for neutrino energy reconstruction. The techniques are very powerful and allow to discriminate in an efficient way signal from background.