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

Journal Article · · Journal of Instrumentation
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  1. Fermi National Laboratory
  2. Yale University
  3. UNKNOWN
  4. University of Texas
  5. Universitat Bern
  6. Fermi National Lab
  7. University of Oxford, UK
  8. TUBITAK Space Technologies, Turkey
  9. Brookhaven National Laboratory
  10. Lancaster University
  11. Kansas State University
  12. Massachusetts Institute of Technology
  13. Columbia University
  14. BATTELLE (PACIFIC NW LAB)
  15. SLAC National Accelerator Laboratory
  16. University of Oxford
  17. University of Pittsburgh
  18. SLAC National Laboratory
  19. University of Bern, Switzerland
  20. University of Cambridge
  21. Syracuse University
  22. University of Chicago
  23. VISITORS
  24. Los Alamos National Laboratory
  25. University of Bern
  26. Fermilab
  27. University of Cincinnati
  28. Univerisity of Oxford
  29. University of manchester
  30. University of Manchester
  31. Center for Neutrino Physics
  32. University of Bern Switerland
  33. Illinois Insitute of Technology
  34. Los Alamos National Lab
  35. Center of Neutrino Physics
  36. Illinois Institute of Technology
  37. New Mexico State University
  38. Brookhaven National Lab
  39. University of Michigan
  40. Saint Mary's University of Minnesota
  41. Lancaster University UK
  42. university of oxford
  43. Otterbein University
  44. COLUMBIA UNIVERSITY
  45. SLAC National Lab
  46. Los Alamos National Laboratory, Los Alamos, NM
  47. Univeristy of Bern, Switzerland
  48. New Mexico National lab
  49. 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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Cited By (9)

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing journal October 2019
Machine learning at the energy and intensity frontiers of particle physics journal August 2018
Extensive deep neural networks for transferring small scale learning to large scale systems journal January 2019
Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation journal July 2018
Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach journal March 2019
Context-enriched identification of particles with a convolutional network for neutrino events journal October 2019
Machine learning and the physical sciences journal December 2019
Extensive deep neural networks for transferring small scale learning to large scale systems text January 2017
Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation text January 2018

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