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Multi-channel analyzer based on a novel pulse fitting analysis method

A novel pulse fitting analysis (PFA) method is presented for the acquisition of nuclear spectra. The charging process of the feedback capacitor in the resistive feedback charge-sensitive preamplifier is equivalent to the impulsive pulse, and its impulse response function (IRF) can be obtained by non...

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Bibliographic Details
Published in:Nuclear engineering and technology 2022-06, Vol.54 (6), p.2023-2030
Main Authors: Wang, Qingshan, Zhang, Xiongjie, Meng, Xiangting, Wang, Bao, Wang, Dongyang, Zhou, Pengfei, Wang, Renbo, Tang, Bin
Format: Article
Language:English
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Summary:A novel pulse fitting analysis (PFA) method is presented for the acquisition of nuclear spectra. The charging process of the feedback capacitor in the resistive feedback charge-sensitive preamplifier is equivalent to the impulsive pulse, and its impulse response function (IRF) can be obtained by non-linear fitting of the falling edge of the nuclear pulse. The integral of the IRF excluding the baseline represents the energy deposition of the particles in the detector. In addition, since the non-linear fitting process in PFA method is difficult to achieve in the conventional architecture of spectroscopy system, a new multi-channel analyzer (MCA) based on Zynq SoC is proposed, which transmits all the data of nuclear pulses from the programmable logic (PL) to the processing system (PS) by high-speed AXI-Stream in order to implement PFA method with precision. The linearity of new MCA has been tested. The spectrum of 137Cs was obtained using LaBr3(Ce) scintillator detector, and was compared with commercial MCA by ORTEC. The results of tests indicate that the MCA based on PFA method has the same performance as the commercial MCA based on pulse height analysis (PHA) method and excellent linearity for γ-rays with different energies, which infers that PFA method is an effective and promising method for the acquisition of spectra. Furthermore, it provides a new solution for nuclear pulse processing algorithms involving regression and iterative processes.
ISSN:1738-5733
DOI:10.1016/j.net.2021.12.019