AI Article Synopsis
- We created a deep learning model that can handle multiple tasks at once, specifically estimating pulse height and distinguishing between different pulse shapes for overlapping neutron and gamma signals.
- Our model outperformed single-task models by providing better accuracy in identifying neutrons and maintaining stable count rates with minimal signal loss.
- This model is useful for a dual radiation scintillation detector, allowing for better identification and analysis of different radioisotopes.
Article Abstract
We developed a multi-tasking deep learning model for simultaneous pulse height estimation and pulse shape discrimination for pile-up n/γ signals. Compared with single-tasking models, our model showed better spectral correction performance with higher recall for neutrons. Further, it achieved more stable neutron counting with less signal loss and a lower error rate in the predicted gamma ray spectra. Our model can be applied to a dual radiation scintillation detector to discriminatively reconstruct each radiation spectrum for radioisotope identification and quantitative analysis.
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| http://dx.doi.org/10.1016/j.apradiso.2023.110880 | DOI Listing |
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