Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
- Fermi National Laboratory
- Yale University
- UNKNOWN
- University of Texas
- Universitat Bern
- Fermi National Lab
- University of Oxford, UK
- TUBITAK Space Technologies, Turkey
- Brookhaven National Laboratory
- Lancaster University
- Kansas State University
- Massachusetts Institute of Technology
- Columbia University
- BATTELLE (PACIFIC NW LAB)
- SLAC National Accelerator Laboratory
- University of Oxford
- University of Pittsburgh
- SLAC National Laboratory
- University of Bern, Switzerland
- University of Cambridge
- Syracuse University
- University of Chicago
- VISITORS
- Los Alamos National Laboratory
- University of Bern
- Fermilab
- University of Cincinnati
- Univerisity of Oxford
- University of manchester
- University of Manchester
- Center for Neutrino Physics
- University of Bern Switerland
- Illinois Insitute of Technology
- Los Alamos National Lab
- Center of Neutrino Physics
- Illinois Institute of Technology
- New Mexico State University
- Brookhaven National Lab
- University of Michigan
- Saint Mary's University of Minnesota
- Lancaster University UK
- university of oxford
- Otterbein University
- COLUMBIA UNIVERSITY
- SLAC National Lab
- Los Alamos National Laboratory, Los Alamos, NM
- Univeristy of Bern, Switzerland
- New Mexico National lab
- Fermi Natinonal Lab
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 identifi-cation 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.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1513232
- Report Number(s):
- PNNL-SA-123442
- Journal Information:
- Journal of Instrumentation, Vol. 12, Issue 3
- Country of Publication:
- United States
- Language:
- English
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