# A Convolutional Neural Network Neutrino Event Classifier

arXiv: https://arxiv.org/abs/1604.01444 · HTML: https://mlsanity.com/paper/1604.01444/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 26 of 26)

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- [Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks](https://mlsanity.com/paper/2303.08812/) (2023)
- [Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers](https://mlsanity.com/paper/2303.06201/) (2023)
- [Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics](https://mlsanity.com/paper/2509.08461/) (2025)
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- [Graph Neural Network for Neutrino Physics Event Reconstruction](https://mlsanity.com/paper/2403.11872/) (2024)
- [Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers](https://mlsanity.com/paper/2504.08182/) (2025)
- [Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training](https://mlsanity.com/paper/2604.07037/) (2026)
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- [Deep Learning strategies for ProtoDUNE raw data denoising](https://mlsanity.com/paper/2103.01596/) (2021)
- [Segmentation of EM showers for neutrino experiments with deep graph neural networks](https://mlsanity.com/paper/2104.02040/) (2021)
- [Domain-informed neural networks for interaction localization within astroparticle experiments](https://mlsanity.com/paper/2112.07995/) (2021)
- [Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizations](https://mlsanity.com/paper/2303.03183/) (2023)
- [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://mlsanity.com/paper/2408.08474/) (2024)
- [Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experiments](https://mlsanity.com/paper/2508.19376/) (2025)
- [Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectors](https://mlsanity.com/paper/2510.09659/) (2025)
- [SparsePixels: Efficient Convolution for Sparse Data on FPGAs](https://mlsanity.com/paper/2512.06208/) (2025)

## 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.