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

Aug 4, 2026

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Machine learning algorithms are developed to distinguish scintillation pulses from an organic plastic-liquid phoswich detector. The key difficulty is separating signals produced by organic scintillators that share similar pulse shapes and very short decay times. With a single-readout phoswich configuration, the study identifies gamma signals from two scintillating components. A Boosted Decision Tree achieves a maximum discrimination power reported as 3.02 ± 0.85 standard deviation in the 950 keV energy region, supporting efficient low-background radiation detection.

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Abstract

Introduction

Materials and Methods

Experimental setup

arXiv :2403 .03392v1 [physics .ins-det] 6 Mar 2024 Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques Yujin Lee, Jinyoung Kim, Byoung-cheol Koh, and Chang Hyon Ha Department of Physics, Chung-Ang University, Seoul 06974, Republic of Korea Young Soo Yoon Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea Abstract We developed machine learning algorithms for distinguishing scintillation signals from a plasticliquid 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 γ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3 .02 ± 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities. Keywords: I. INTRODUCTION Organic scintillators are indispensable tools across diverse technological and scientific realms, from environmental monitoring to the investigation of rare nuclear events [1–3] . Their light-emitting property, activated when constituent molecules undergo deexcitation from ionizing radiations such as alphas, betas, gamma-induced electrons, neutron-induced protons, and cosmic-ray muons makes them vital in radiation detection, medical imaging, and nuclear physics research [4, 5] . Particularly, they are crucial elements in the quest to detect particles like dark matter and neutrinos, demanding highly sensitive detectors for the detection of ultra-low levels of radiation [6, 7] . The appeal of organic scintillators lies in their fast decay time, ease of fabrication, and scalability, distinguishing them as a preferred choice in comparison to other scintillator options [5] Dark matter particles and neutrinos, ubiquitous yet weakly interacting, pose significant challenges in measurement due to their elusive nature and poorly understood physical properties. Therefore, large-scale experiments are essential to study these particles comprehensively. Organic scintillators play a crucial role in rare decay experiments, enhancing detector capabilities through improved positioning of particle interactions. For instance, in dark matter direct detection experiments, precise positioning aids in background rejection by distinguishing radioactivity in the surrounding environment from that of the main target material [7] . Segmentation concepts, particularly beneficial in short baseline neutrino experiments, exploit variable oscillation baselines within a single experiment [8, 9] . This study introduces a novel single-readout detector called a phoswich (phosphor sandwich) [10] where plastic scintillator (PS) serves as the inner target, and liquid scintillator (LS) acts as the outer guard. Traditional light sensors such as Photomultiplier Tubes (PMTs), containing a significant amount of natural U/Th/K radioactivities, tend to elevate the background level, particularly when placed in close proximity to the target material. The phoswich target-guard approach can improve the target’s background contamination by physically distancing PMTs and identifying interactions detected in the guard material, which also serves as a light guide. In addition to pinpointing interactions, the identification of particles such as gammas, alphas, and neutrons with signal shape analyses could enhance detector sensitivity with a reduced number of PMTs per target volume. Despite the challenge posed by the similar decay times of a few nanoseconds for the two organic scintillators within the phoswich setup, this study utilizes machine learning techniques to effectively discriminate gamma signals between the two scintillators, marking an important first step towards a large-scale position-sensitive low-background detector con

FAQs

What problem does the study address in the phoswich detector?

It addresses how to discriminate scintillation signals from two organic components when their pulse shapes and decay times are similar.

What detector configuration is used for the analysis?

The study uses a single-readout phoswich where an inner plastic scintillator and an outer liquid scintillator share common readout through photomultiplier tubes.

Which machine learning method provides the reported discrimination performance?

A Boosted Decision Tree is used, with the maximum discrimination power reported in the 950 keV region as 3.02 ± 0.85 standard deviation.

Aug 4, 2026

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