Speaker
Description
Cosmic rays at ground level are dominated by high-energy muons and are traditionally identified using coincidence techniques that require multiple detectors. In this work, a machine-learning-based approach is proposed for single-detector cosmic-ray muon identification using a plastic scintillation detector. Waveform data were acquired from gamma-ray events obtained using standard gamma-emitting sources and from cosmic-ray muons identified through a conventional coincidence setup for labeling purposes. The signals were digitized using a fast waveform digitizer and directly used as inputs to a machine learning model.
Machine learning was employed to discriminate cosmic-ray muons from gamma background based on waveform characteristics. When applied to background radiation measurements, the trained model successfully extracted the cosmic-ray muon component and reproduced the characteristic muon energy deposition peak in the plastic scintillator, consistent with expectations for minimum ionizing particles. The machine-learning-based results show good agreement with those obtained using traditional coincidence techniques. These results demonstrate that machine learning enables reliable cosmic-ray muon identification using a single plastic scintillation detector, offering a simplified and cost-effective alternative to hardware-intensive coincidence systems for radiation monitoring and cosmic-ray studies.
| Minioral | Yes |
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| IEEE Member | Yes |
| Are you a student? | No |