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Title: A convolutional neural network neutrino event classifier

Journal Article · · Journal of Instrumentation
 [1];  [2];  [3];  [4];  [5];  [4];  [3];  [5];  [1];  [2]
  1. Univ. of Cincinnati, Cincinnati, OH (United States)
  2. College of William and Mary, Williamsburg, VA (United States)
  3. Univ. of Minnesota, Minneapolis, MN (United States)
  4. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  5. Indiana Univ., Bloomington, IN (United States)
+ Show Author Affiliations

Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1322151
Report Number(s):
FERMILAB-PUB-16-082-ND; arXiv:1604.01444; 1444342
Journal Information:
Journal of Instrumentation, Vol. 11, Issue 09; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 125 works
Citation information provided by
Web of Science

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Cited By (17)

Deep convolutional neural networks for eigenvalue problems in mechanics journal January 2019
Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks journal July 2018
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing journal October 2019
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning journal June 2017
Enabling real-time multi-messenger astrophysics discoveries with deep learning journal October 2019
End-to-End Event Classification of High-Energy Physics Data journal September 2018
Context-enriched identification of particles with a convolutional network for neutrino events journal October 2019
Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks journal May 2020
Pileup mitigation at the Large Hadron Collider with graph neural networks journal July 2019
Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector journal July 2018
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning text January 2017
Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks text January 2020
Search for active-sterile neutrino mixing using neutral-current interactions in NOvA text January 2017
Extensive deep neural networks for transferring small scale learning to large scale systems text January 2017
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks preprint January 2018
Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks text January 2019
Enabling real-time multi-messenger astrophysics discoveries with deep learning text January 2019

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
particle identification methods
pattern recognition
cluster finding
calibration and fitting methods
Neutrino detectors
particle tracking detectors