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. • Multi-tasking deep learning model for piled-up n/γ signals. • Simultaneous pulse height estimation and pulse shape discrimination. • Better spectral correction performance with a higher recall of neutrons. • Stable neutron counting with low signal loss and low error in predicted gamma spectra. [ABSTRACT FROM AUTHOR]