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Vertex reconstruction of neutrino interactions using deep learning

by Adam Terwilliger, Gabriel Perdue, David Isele, Robert M Patton, Steven R Young
Publication Type
Conference Paper
Book Title
Neural Networks (IJCNN), 2017 International Joint Conference on
Publication Date
Page Numbers
2275 to 2281
Conference Name
International Joint Conference on Neural Networks (IJCNN 2017)
Conference Location
Anchorage, Alaska, United States of America
Conference Date

Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend on vertex reconstruction -- finding the origin of a neutrino interaction using data from the detector, which can be represented as images. Traditionally, this has been accomplished by utilizing methods that identify the tracks coming from the interaction. However, these methods are not ideal for interactions where an abundance of tracks and cascades occlude the vertex region. Manual algorithm engineering to handle these challenges is complicated and error prone. Deep learning extracts rich, semantic features directly from raw data, making it a promising solution to this problem. In this work, deep learning models are presented that classify the vertex location in regions meaningful to the domain scientists improving their ability to explore more complex interactions.