mlsanityData to 7 Oct 2026

A Convolutional Neural Network Neutrino Event Classifier

arXiv 1604.01444 ↗First posted 5 Apr 2016hep-ex cs.CVcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2016.

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Citing papers per year: 2021: 9; 2022: 4; 2023: 4; 2024: 3; 2025: 5; 2026: 1.

26 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  3. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  4. 2021Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networkscited by 14
  5. 2021A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatorycited by 13
  6. 2022Explainable AI for High Energy Physicscited by 7
  7. 2021Machine-learning enhanced dark soliton detection in Bose-Einstein condensatescited by 5
  8. 2023Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networkscited by 4
  9. 2023Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformerscited by 3
  10. 2025Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physicscited by 3
  11. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  12. 2022Pulse Shape Simulation and Discrimination using Machine-Learning Techniquescited by 2
14 more
  1. 2023Extending Explainable Boosting Machines to Scientific Image Datacited by 2
  2. 2024Graph Neural Network for Neutrino Physics Event Reconstructioncited by 2
  3. 2025Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chamberscited by 2
  4. 2026Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-trainingcited by 2
  5. 2022Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic fieldcited by 1
  6. 2024Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detectorcited by 1
  7. 2021Deep Learning strategies for ProtoDUNE raw data denoisingcited by 0
  8. 2021Segmentation of EM showers for neutrino experiments with deep graph neural networkscited by 0
  9. 2021Domain-informed neural networks for interaction localization within astroparticle experimentscited by 0
  10. 2023Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizationscited by 0
  11. 2024Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolutioncited by 0
  12. 2025Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experimentscited by 0
  13. 2025Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectorscited by 0
  14. 2025SparsePixels: Efficient Convolution for Sparse Data on FPGAscited by 0