Deep neural networks help to identify the neutrinoless double beta decay signal
(PHYSorg.com Physics)

A group of researchers from Shanghai Jiao Tong University and Peking University greatly improved the discrimination power of tracks from different particles passing through the gaseous detector with the help of deep convolutional neural networks. The work will help to improve the sensitivity of detection for the PandaX-III neutrinoless double beta decay experiment, and deepen our knowledge of the nature of neutrinos.