PCA-based neutron/gamma discrimination with organic scintillators

Principal component analysis (PCA) has been used for n/? discrimination, but the influencing factors were not considered. In this paper, the influence of dataset construction was analyzed with pulses from a liquid scintillator. Four datasets were constructed according to the normalization factor and the start point. After dimensionality reduction, the distributions of the datasets were examined and figure of merits were calculated. Results show that the "tail-peak" dataset has the best performance and outperforms charge comparison method. It means that to optimize the performance and adaptability of PCA-based n/? discrimination, the dataset should be constructed with the falling edge integration as the normalization factor and the pulse peak as the start point.