A Convolutional Neural Network Neutrino Event Classifier

2016/04/05 by A. Aurisano, A. Radovic, D. Rocco +7 · 1 voice
Computer Science · Physics and Astronomy · #cs.CV #hep-ex

paper · pdf · doi:10.1088/1748-0221/11/09/p09001

published as 2016 JINST 11 P09001 · 23 pages, 12 figures

arxiv published 2016/04/05 · arxiv created 2016/08/12 · arxiv updated 2016/08/12

Abstract

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.

Discussions

• .@novaexperiment's paper on using Convolutional Neural Networks for event selection! https://arxiv.org/pdf/1604.01444v2.pdf Good to see CNNs in physics! [bsky, 0 points, 0 comments]

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