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Cosmic Ray Background Removal With Deep Neural Networks in SBND
R Acciarri1, C Adams2, C Andreopoulos3,4
1Fermi National Accelerator Laboratory, Batavia, IL, United States.
Frontiers in Artificial Intelligence
|September 10, 2021
Summary
Deep learning effectively removes cosmic particle backgrounds in surface-level liquid argon time projection chambers. This technique distinguishes cosmic-induced noise from genuine neutrino interactions using full detector images.
Area of Science:
- Particle Physics
- Detector Technology
- Artificial Intelligence
Background:
- Surface-level liquid argon time projection chambers (LArTPCs) face significant background noise from cosmic muons and particles.
- This cosmic interference dominates event triggers and data, obscuring rare neutrino interactions.
Purpose of the Study:
- To develop and demonstrate a deep learning method for mitigating cosmic ray backgrounds in LArTPC data.
- To accurately differentiate between cosmic particle activity and neutrino-induced events within detector images.
Main Methods:
- Application of deep learning techniques to full detector images from the SBND detector.
- Pixel-level analysis to classify recorded activity as either cosmic or neutrino-origin.
Main Results:
- Successful identification and removal of cosmic particle signals from LArTPC data.
- Demonstration of deep learning's capability to enhance neutrino event detection in noisy environments.
Conclusions:
- Deep learning offers a powerful solution for background reduction in surface-based neutrino experiments.
- This method significantly improves the purity of neutrino interaction data, crucial for physics analyses.
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