A Convolutional Neural Network Neutrino Event Classifier
This work addresses the challenge of neutrino event classification for high-energy physics experiments, representing an incremental improvement by adapting established CNN techniques to a specific domain.
The paper tackled the problem of identifying particle interactions in neutrino detectors by applying convolutional neural networks (CNNs) to the NOvA detector, resulting in an algorithm (CVN) that outperforms existing methods without requiring detailed reconstruction.
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.