Pulse height estimation and pulse shape discrimination in pile-up neutron and gamma ray signals from an organic scintillation detector using multi-task learning.

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.

Estimation of ambient dose equivalent rate with a plastic scintillation detector using the least-square and first-order methods-based G(E) function.

Dose-rate monitoring instruments measure the ambient dose equivalent and hence are crucial for protecting workers from radiation exposure. Although plastic scintillation detectors (PSDs) are ideal equipment for dosimetry, they are rarely used owing to the lower detection efficiency than other scintillation detectors. In this study, we acquired ten types of G(E) functions to utilize a PSD in spectroscopic dosimetry using the least-square and first-order methods. The energy response of PSD was much improved in terms of dose evaluation.

Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning.

Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties.

Reconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder.

Plastic scintillation detectors are widely utilized in radiation measurement because of their unique characteristics. However, they are generally used for counting applications because of the energy broadening effect and the absence of a photo peak in their spectra. To overcome their weaknesses, many studies on pseudo spectroscopy have been reported, but most of them have not been able to directly identify the energy of incident gamma rays. In this paper, we propose a method to reconstruct Compton edges in plastic gamma spectra using an artificial neural network for direct pseudo gamma spectroscopy. Spectra simulated using MCNP 6.2 software were used to generate training and validation sets. Our model was trained to reconstruct Compton edges in plastic gamma spectra. In addition, we aimed for our model to be capable of reconstructing Compton edges even for spectra having poor counting statistics by designing a dataset generation procedure. Minimum reconstructible counts for single isotopes were evaluated with metric of mean averaged percentage error as 650 for 60Co, 2000 for 137Cs, 3050 for 22Na, and 3750 for 133Ba. The performance of our model was verified using the simulated spectra measured by a PVT detector. Although our model was trained using simulation data only, it successfully reconstructed Compton edges even in measured gamma spectra with poor counting statistics.