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Related Experiment Videos

Using machine learning to improve neutron identification in water Cherenkov detectors.

Blair Jamieson1, Matt Stubbs2, Sheela Ramanna2

  • 1Physics Department, University of Winnipeg, Winnipeg, MB, Canada.

Frontiers in Big Data
|October 17, 2022
PubMed
Summary

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Machine learning models, including XGBoost and GCNs, enhance neutron capture detection in Hyper-Kamiokande

Area of Science:

  • Particle Physics
  • Experimental Physics
  • Neutrino Physics

Background:

  • Gadolinium is added to Water Cherenkov detectors like Hyper-Kamiokande to improve neutron detection.
  • Neutron detection aids in distinguishing neutrino and anti-neutrino interactions and reduces backgrounds for proton decay searches.
  • Neutron signals are subtle and can be mistaken for background noise from muon spallation.

Purpose of the Study:

  • To optimize neutron capture detection in the Hyper-Kamiokande intermediate water Cherenkov detector (IWCD) using machine learning.
  • To benchmark machine learning models against traditional statistical methods for improved classification accuracy.

Main Methods:

  • Development and application of boosted decision tree (XGBoost), graph convolutional network (GCN), and dynamic graph convolutional neural network (DGCNN) models.
Keywords:
graph neural networksmachine learningneutrino physicsparticle physicswater Cherenkov detector

Related Experiment Videos

  • Benchmarking machine learning models against a statistical likelihood-based approach.
  • Feature engineering and analysis using SHAP (SHapley Additive exPlanations) to understand model decision-making.

    • Main Results:

    • Machine learning models achieved up to a 10% increase in classification accuracy compared to the statistical approach.
    • SHAP analysis provided insights into key features driving event type classification.
    • The study utilized a dataset of approximately 1.6 million simulated particle gun events.

    Conclusions:

    • Machine learning techniques offer a significant improvement in neutron capture detection for neutrino experiments.
    • Further development with more realistic datasets is necessary for real-world data analysis.
    • Consideration of class imbalance techniques may be required for future analyses of experimental data.