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

Abstract

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.

References in corpus (11)

• The GENIE Neutrino Monte Carlo Generator
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• The NuMI Neutrino Beam
• Jet-Images -- Deep Learning Edition
• A Convolutional Neural Network Neutrino Event Classifier
• New constraints on oscillation parameters from ${\nu }_e$ appearance and ${\nu }_{\mu }$ disappearance in the NOvA experiment
• Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
• A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• Background rejection in NEXT using deep neural networks
• Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks
• Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

Cited by in corpus (11)

• Dark Matter Annihilation to Neutrinos
• Neutrino mass ordering obscured by non-standard interactions
• A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• A Review on Machine Learning for Neutrino Experiments
• Expanding neutrino oscillation parameter measurements in NOvA using a Bayesian approach
• Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
• Dual-Baseline Search for Active-to-Sterile Neutrino Oscillations in NOvA
• Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
• Graph Neural Network for Neutrino Physics Event Reconstruction
• Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs
• Neutral pion reconstruction using machine learning in the MINERvA experiment at $⟨E_{\nu }⟩\sim 6$ GeV