  Organic scintillators, such as plastic scintillators, are widely used to
detect fast neutrons and gamma rays. The EJ-276 scintillator offers a versatile
solution for detecting fast neutrons and gamma rays simultaneously, making it
ideal for mixed neutron-gamma field detection applications. This study
evaluates the Pulse Shape Discrimination (PSD) capabilities of the EJ-276
scintillator paired with silicon photomultiplier (SiPM) array readouts.
Integrating the 1-inch EJ-276 scintillator with SiPM arrays achieved a Figure
of Merit (FOM) of 1.13 at an energy threshold of 200 keVee (electron
equivalent). However, using the Charge Comparison Method (CCM) to distinguish
between neutrons and gamma rays was challenging, especially at energies below
200 keVee. To improve low-energy resolution, the Convolutional Neural Network
(CNN) approach was adopted. The InceptionTime and EfficientNetV2 models were
developed, using one-dimensional time series and two-dimensional matrix image
inputs, respectively. The transformation from one-dimensional arrays to
two-dimensional images was achieved using three techniques: Gramian Angular
Summation Field(GASF), Recurrence Plot(RP), and Relative Position Matrix(RPM).
These methods demonstrated high accuracy at energy levels above 200 keVee. At
lower energy regions, CNN methods, particularly the InceptionTime model,
outperformed CCM methods. Notably, CNN methods reached accuracies of 96.79% and
98.33% in the 0-100 keVee and 100-200 keVee ranges, respectively, significantly
higher than the 85.49% and 94.56% achieved by CCM, representing improvements of
13.22% and 3.99%. These results highlight the superior performance of CNN
methods in differentiating between neutrons and gamma rays, especially in
low-energy regions.
