Scaling studies for deep learning in Liquid Argon Time Projection Chamber event classification

• Main Authors: Strube Jan, Bhattacharya Kolahal, Church Eric, Daily Jeff, Malachi Schram, Charles Siegel, Kevin Wierman
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: EPJ Web of Conferences
• Online Access: https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_06016.pdf

Measurements in Liquid Argon Time Projection Chamber neutrino detectors feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to these event images is challenging, due to the large size of the events, more two orders of magnitude larger than images found in classical challenges like MNIST or ImageNet. This leads to extremely long training cycles, which slow down the exploration of new network architectures and hyperpa-rameter scans to improve the classification performance. We present studies of scaling an LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out in simulated events in the Micro-BooNE detector.