Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques

TL;DR

This paper presents machine learning algorithms, including Boosted Decision Trees, to effectively discriminate scintillation signals in an organic phoswich detector, improving radiation detection accuracy.

Contribution

The study introduces a novel machine learning approach for pulse shape discrimination in a single-readout organic scintillation detector, achieving high discrimination power.

Findings

Boosted Decision Tree achieved discrimination power of 3.02 standard deviations.

Successfully distinguished gamma signals from two scintillating components.

Enhanced radiation detection capabilities in a compact detector setup.

Abstract

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified $\gamma$ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 $\pm$ 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.