arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.03392physics.ins-detastro-ph.IMhep-ex
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使用机器学习技术的有机闪烁磷光探测器脉冲形状甄别

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

Yujin Lee, Jinyoung Kim, Byoung-cheol Koh, Young Soo Yoon, Chang Hyon Ha

AI总结:

针对形状相似、衰减时间短的有机闪烁体信号难甄别的问题,研究开发机器学习算法用于单读出磷光探测器的脉冲形状甄别,其提升决策树算法在950 keV区间达3.02±0.85标准差的最大甄别能力,为自屏蔽及增强辐射探测能力提供高效方案。

摘要:

我们开发了用于区分塑料-液体耦合探测器(即phoswich(磷光探测器))闪烁信号的机器学习算法。挑战在于甄别形状相似、衰减时间短的有机闪烁体信号。利用单读出磷光探测器,我们成功识别出两种闪烁组分的γ辐射信号。我们的Boosted Decision Tree(提升决策树)算法在950 keV区间展现出3.02±0.85标准差的最大甄别能力,为自屏蔽提供了高效方案,并增强了辐射探测能力。

英文摘要:

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 $γ$ 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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