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Abstract: Scintillator detectors, widely used in nuclear medicine and industrial applications such as radiation monitoring and material analysis, are sensitive to both neutrons and gamma rays (n/γ). A key challenge in neutron detection is minimizing gammaray interference to ensure accurate measurements. Neutron-gamma discrimination is difficult because the two particle types often produce overlapping signals in scintillator detectors, with similar pulse amplitudes but subtle differences in shape and timing. Traditional methods struggle to distinguish these subtle features, leading to misclassification and reduced detection accuracy.To address this, we propose a deep neural network (DNN)-based approach combined with pulse shape discrimination (PSD) techniques to achieve high-precision particle discrimination in mixed n/γ fields. Leveraging DNN‘s ability to learn complex patterns, our method effectively classifies neutron and gamma-ray pulses. The trained DNN model was evaluated against traditional discrimination algorithms, including the charge comparison method, rise-time analysis, frequency-domain gradient analysis, and K-means clustering. Quantitative results demonstrate a discrimination accuracy of 99%, significantly outperforming conventional techniques. Furthermore, the proposed DNN method not only enhances discrimination reliability in mixed radiation fields but also reduces processing time compared to existing methods, making it suitable for real-time applications in medical imaging and industrial neutron detection.
Keywords: neutroni in gama žarki, globoka nevronska mreža, razlikovanje oblike impulza
Published in DiRROS: 17.06.2026; Views: 320; Downloads: 336
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