Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

by Dominick Rocco

Institution: University of Minnesota
Year: 2016
Keywords: Convolutional Neural Network; Deep Learning; Neutrino; Neutrino Oscillation
Posted: 02/05/2017
Record ID: 2117037
Full text PDF: http://hdl.handle.net/11299/180223


The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of Δ m232 and θ23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74 × 1020 POT, 14 kt equivalent) to provide new best-fit measurements of sin2(θ23) = 0.43 (with a statistically-degenerate compliment near 0.60) and |Δ m232| = 2.48 × 10-3~{eV}2.