Pileup recovery algorithms for digital gamma ray spectroscopy

Abstract

This paper presents algorithms for overcoming a common problem of gamma ray spectroscopy, namely the peak pileup recovery problem. Three different approaches are studied and evaluated within a spectroscopy system. The algorithms are evaluated by the means of parameters error and fitting error calculations. The first approach is a direct search based on Nelder-Mead technique without any derivatives in order to find the local minimum points. A Gaussian shape in conjunction with the peak height and its position of each pulse are used to construct the pulse. So, the main pulse parameters such as peak amplitude, position and width can be determined. The second algorithm is based on the nonlinear least square method. This approach has accuracy of recovering the original pulses with mean square error of 4.7306x10-12. In this paper another technique is tried. This technique which is proposed as third algorithm is based on a maximum peak search method combined with the first derivative method to determine peak position of each pulse. Comparison among these approaches is conducted in terms of parameters errors. The pulse parameters have been calculated and compared with the actual one. The second approach shows the best accuracy, for determining peak height and position, but the width parameter scored the highest error.

Development of low-cost digital gamma spectrometer using ARM Cortex-M Type Microcontroller Unit

“…The first filter is the moving average filter which suppresses the noise only and it is simple and fast [17]. The other filter is the trapezoidal shaping filter which is a popular filter in radiation spectrometry due to its capability to improve the energy resolution, reduce the pulse pile-up events, and suppress the noise [19][20][21][22]. In [12], Boorboor et al used the trapezoidal shaping filter in their implementation.…”

An efficient digital pulse processing approach for identification of BGO and LSO scintillator crystals

“…This defect can be detected using deep learning technique at higher rates [21]. However, the recovery of pile-up pulses can be done using our previous proposed implemented algorithms in [22,23].…”

Hardware Implementation for Pileup Correction Algorithms in Gamma_Ray Spectroscopy

“…1. The system main components are as follows: a scintillation detector NaI (TI), which used to recognize the input signal from radiation source 137 Cs; the main block of the digital data acquisition system (DAS), which consists of: amplifier, high speed ADC digitizer, and monitoring or personal computer (PC) for data reconstruction and display [4], [10]. …”

“…These advantages include simple and fast implementation of complex pile up recovery algorithms used for signal processing. The authors in [4], [5] discussed three different approaches for peak pileup recovery problem in GRS. The first approach depends on the inverse matrix algorithm and it is more accurate than other algorithms.…”