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

Acciarri, R. and An, R. and Asaadi, J. and Auger, M. and Bagby, L. and Baller, B. and Barr, G. and Bass, M. and Bay, F. and Bishai, M. and Blake, A. and Bolton, T. and Bugel, L. and Camilleri, L. and Caratelli, D. and Carls, B. and Fernandez, R. Castillo and Cavanna, F. and Church, E. and Cianci, D. and Collin, G. H. and Conrad, J. M. and Convery, M. and Crespo-Anadón, J. I. and Tutto, M. Del and Devitt, D. and Dytman, S. and Eberly, B. and Ereditato, A. and Sanchez, L. Escudero and Esquivel, J. and Fleming, B. T. and Foreman, W. and Furmanski, A. P. and Garvey, G. T. and Genty, V. and Goeldi, D. and Gollapinni, S. and Graf, N. and Gramellini, E. and Greenlee, H. and Grosso, R. and Guenette, R. and Hackenburg, A. and Hamilton, P. and Hen, O. and Hewes, J. and Ho, J. and Lister, A. and Nowak, J. (2017) Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. Journal of Instrumentation, 12. ISSN 1748-0221

Other (pdf)
1611.05531v1 - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (7MB)


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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Instrumentation
Additional Information:
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
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
12 Feb 2018 13:39
Last Modified:
03 Jul 2020 02:46