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    Rights statement: This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/12/03/P03011

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Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

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  • MicroBooNE Collaboration
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Article numberP03011
<mark>Journal publication date</mark>14/03/2017
<mark>Journal</mark>Journal of Instrumentation
Volume12
Number of pages57
Publication statusPublished
Original languageEnglish

Abstract

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

Bibliographic note

This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/12/03/P03011