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Performance of Linear PSD Methods on α/γ Discrimination for LaBr3 : Ce Scintillation Detectors with Different Sampling Properties

机译:线性PSD方法在不同采样特性的LaBr 3 :Ce闪烁探测器α/γ识别中的性能

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LaBr3 scintillation detectors are widely used in energy and TOF measurement for its excellent energy resolution and time resolution. However, due to the effect of intrinsic radioactive isotopes like Ac-227 in LaBr3, it’s important to discriminate the alpha background from high energy gamma-rays in low activity measurement. Generally, the pulse shape discrimination (PSD) methods are necessary to be deployed in this measurement situation to achieve this target. Therefore, how to choose a PSD algorithm and corresponding data acquisition system (DAQ) naturally becomes a critical problem to settle down. In this paper, the relationship between the classification performance of different PSD algorithms and digitizers’ sampling properties (sampling rate and effective number of bits) has been researched based on a LaBr3 scintillation detector. A self-developed integrated digitizer system with 6 different ADCs is deployed to digitize the waveforms, and three PSD algorithms, charge comparison method (CCM), least square for classification method (LS) and Fisher’s linear discriminant analysis (LDA), based on linear model are also applied. With the LDA method and a 125 MSPS 16-Bit ADC, the figure-of-merits (FoM) value is , which is similar to the discrimination capability from LS but much better than CCM. The results of this paper may be helpful for the developers of detector system to make a trade-off among DAQ’s sampling properties, desirable discrimination performance and cost of system.
机译:溴化镧 3 闪烁探测器因其出色的能量分辨率和时间分辨率而广泛用于能量和TOF测量。但是,由于LaBr中固有的放射性同位素(例如Ac-227)的作用 3 ,在活动度低的测量中,区分高能伽马射线的alpha背景非常重要。通常,在这种测量情况下必须采用脉冲形状识别(PSD)方法来实现此目标。因此,如何选择PSD算法及相应的数据采集系统(DAQ)自然成为解决的关键问题。本文基于LaBr,研究了不同PSD算法的分类性能与数字化仪采样属性(采样率和有效位数)之间的关系。 3 闪烁探测器。部署了具有6个不同ADC的自行开发的集成数字化仪系统,以对波形进行数字化,并基于线性算法,采用了三种PSD算法,电荷比较方法(CCM),最小二乘分类法(LS)和Fisher线性判别分析(LDA)模型也适用。使用LDA方法和125 MSPS 16位ADC时,品质因数(FoM)值为,这与LS的判别能力相似,但比CCM更好。本文的结果可能有助于检测器系统的开发人员在DAQ的采样属性,理想的辨别性能和系统成本之间进行权衡。

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