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

Conference · Tue Sep 17 00:00:00 EDT 2019

DOI:https://doi.org/10.1051/epjconf/201921406016· OSTI ID:1595776

Strube, Jan F.  ^[1]; Bhattacharya, Kolahal ^[1]; Church, Eric D. ^[1]; Daily, Jeffrey A.  ^[1]; Schram, Malachi  ^[1]; Siegel, Charles M. ^[1]; Wierman, Kevin J. ^[1]

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1. BATTELLE (PACIFIC NW LAB)

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 chal- lenges 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 MicroBooNE detector.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1595776
• Report Number(s): PNNL-SA-139482
• Resource Relation: Journal Volume: 214; Conference: 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), July 9-13, 2018, Sofia, Bulgaria. EPJ Web of Conferences
• Country of Publication: France
• Language: English