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Title: Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events

Journal Article · · Phys.Rev.
 [1];  [2];  [3];  [3];  [4];  [2];  [1];  [3];  [5];  [4]
  1. Texas U.
  2. Fermilab
  3. Indiana U.
  4. Cincinnati U.
  5. William-Mary Coll.
+ Show Author Affiliations

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.

Research Organization:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
DOE Contract Number:
AC02-07CH11359
OSTI ID:
1573833
Report Number(s):
arXiv:1906.00713; FERMILAB-PUB-19-258-PPD; oai:inspirehep.net:1737736
Journal Information:
Phys.Rev., Vol. D100, Issue 7
Country of Publication:
United States
Language:
English

References (30)

Characteristics of Bubble Chambers journal January 1955
Machine learning at the energy and intensity frontiers of particle physics journal August 2018
Jet-images — deep learning edition journal July 2016
A convolutional neural network neutrino event classifier journal September 2016
CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks journal January 2018
Improved energy reconstruction in NOvA with regression convolutional neural networks journal January 2019
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber journal March 2017
Background rejection in NEXT using deep neural networks journal January 2017
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINER v A experiment journal November 2018
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber journal May 2019
New constraints on oscillation parameters from ν e appearance and ν μ disappearance in the NOvA experiment journal August 2018
Neutrino–nucleus cross sections for oscillation experiments journal December 2017
The NuMI neutrino beam
  • Adamson, P.; Anderson, K.; Andrews, M.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 806 https://doi.org/10.1016/j.nima.2015.08.063
journal January 2016
Event Reconstruction Techniques in NOvA journal December 2015
The GENIE neutrino Monte Carlo generator
  • Andreopoulos, C.; Bell, A.; Bhattacharya, D.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 614, Issue 1 https://doi.org/10.1016/j.nima.2009.12.009
journal February 2010
Geant4—a simulation toolkit
  • Agostinelli, S.; Allison, J.; Amako, K.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3 https://doi.org/10.1016/S0168-9002(03)01368-8
journal July 2003
The NO v A simulation chain journal December 2015
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters journal January 1973
Going deeper with convolutions conference June 2015
ROOT — An object oriented data analysis framework journal April 1997
The art framework journal December 2012
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber text January 2019
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber text January 2017
Going Deeper with Convolutions preprint January 2014
The NuMI Neutrino Beam text January 2015
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber text January 2016
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks text January 2017
A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber text January 2018
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment text January 2018
Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks text January 2018

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