A General-Purpose Digital Pulse Shape Discrimination Algorithm

Abstract

Digital pulse-shape discrimination (PSD) systems are widely used to extract various kinds of information from the shape of output pulses of radiation detectors. Such systems replace the traditional analog PSD circuits with numerical algorithms running on a digital processor which makes them more flexible for implementing various PSD methods. However, the available digital PSD algorithms have been generally tailored to the characteristics of the concerned detectors which limits their general use. Here, for the purpose of building a general-purpose digital PSD module, we present a PSD algorithm that with minimum alterations can be used with a wide range of radiation detectors. Our approach is based on using the cosine similarity measure to quantify the variations in the shape of detectors' pulses with respect to the shape of a unit step pulse. The method is described in details, and the results of the test experiments with different types of radiation detectors, including a boron tetrafluoride (BF3) proportional counter, a liquid scintillation detector, and a high purity germanium (HPGe) detector are shown. The performance of the algorithm is also compared to that of the common rise-time discrimination method.

A pulse selection algorithm for SiPM-coupled CLYC detectors

“…The advancement in fast analog-to-digital converters and digital circuits has opened up new opportunities for improving the measurement performance of various radiation detectors. Algorithms such as the pile-up pulse discriminator, baseline recovery, and pulse amplitude discriminator perform better than analog circuits [13][14][15][16]. Langford et al presented a rise time discrimination method for particle identification and background rejection in 3 He proportional counters for neutron detection [17].…”

A Simplified Correlation Index for Fast Real-Time Pulse Shape Recognition

“…Indeed, pulse shape recognition may be carried out with different methodologies and is used in many contexts where a known signal is acquired for further feature extraction [ 5 ]. In addition, these methodologies have been categorized according to the metrics or algorithms used, such as pulse shape parameters, template comparison, amplification of pulse shape variations, and statistical models [ 6 ].…”

Efficient machine learning approach for optimizing the timing resolution of a high purity germanium detector

“…The availability of fast digitizers have resulted in a slew of methods to increase the timing resolution of HPGe detectors, such as optimizing the parameters of digital time pick-off algorithms [5,13] or implementing novel pulse shape discrimination techniques, which have the additional benefit of being able to determine the γ-ray interaction point in the detector for tracking or to discriminate between single site or multi-site interactions [3,10,12,13,[18][19][20][21][22][23][24][25].…”