Optimal parameter choice of CR–RC~m digital filter in nuclear pulse processing
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摘要
CR–RC~m filters are widely used in nuclear energy spectrum measurement systems. The choice of parameters of a CR–RC~m digital filter directly affects its performance in terms of energy resolution and pulse count rate in digital nuclear spectrometer systems. A numerical recursive model of a CR differential circuit and RC integration circuit is derived, which shows that the shaping result of CR–RC~m is determined by the adjustment parameter(k, it determines the shaping time of the shaper)and the integral number(m). Furthermore, the amplitude–frequency response of CR–RC~m is analyzed, which shows that it is a bandpass filter; the larger the shaping parameters(k and m), the narrower is the frequency band. CR–RC~m digital Gaussian shaping is performed on the actual sampled nuclear pulse signal under different shaping parameters. The energy spectrum of ~(137)Cs is measured based on the LaBr_3(Ce) detector under different parameters. The results show that the larger the shaping parameters(m and k), the closer the shaping result is to Gaussian shape, the wider is the shaped pulse, the higher is the energy resolution, and the lower is the pulse count rate. For the same batch of pulse signals, the energy resolution is increased from 3.8 to 3.5%, and the full energy peak area is reduced from 7815 to 6503. Thus, the optimal shaping parameters are m=3 and k=0:95. These research results can provide a design reference for the development of digital nuclear spectrometer measurement systems.
CR–RC~m filters are widely used in nuclear energy spectrum measurement systems. The choice of parameters of a CR–RC~m digital filter directly affects its performance in terms of energy resolution and pulse count rate in digital nuclear spectrometer systems. A numerical recursive model of a CR differential circuit and RC integration circuit is derived, which shows that the shaping result of CR–RC~m is determined by the adjustment parameter(k, it determines the shaping time of the shaper)and the integral number(m). Furthermore, the amplitude–frequency response of CR–RC~m is analyzed, which shows that it is a bandpass filter; the larger the shaping parameters(k and m), the narrower is the frequency band. CR–RC~m digital Gaussian shaping is performed on the actual sampled nuclear pulse signal under different shaping parameters. The energy spectrum of ~(137)Cs is measured based on the LaBr_3(Ce) detector under different parameters. The results show that the larger the shaping parameters(m and k), the closer the shaping result is to Gaussian shape, the wider is the shaped pulse, the higher is the energy resolution, and the lower is the pulse count rate. For the same batch of pulse signals, the energy resolution is increased from 3.8 to 3.5%, and the full energy peak area is reduced from 7815 to 6503. Thus, the optimal shaping parameters are m=3 and k=0:95. These research results can provide a design reference for the development of digital nuclear spectrometer measurement systems.
CR–RC~m filters are widely used in nuclear energy spectrum measurement systems. The choice of parameters of a CR–RC~m digital filter directly affects its performance in terms of energy resolution and pulse count rate in digital nuclear spectrometer systems. A numerical recursive model of a CR differential circuit and RC integration circuit is derived, which shows that the shaping result of CR–RC~m is determined by the adjustment parameter(k, it determines the shaping time of the shaper)and the integral number(m). Furthermore, the amplitude–frequency response of CR–RC~m is analyzed, which shows that it is a bandpass filter; the larger the shaping parameters(k and m), the narrower is the frequency band. CR–RC~m digital Gaussian shaping is performed on the actual sampled nuclear pulse signal under different shaping parameters. The energy spectrum of ~(137)Cs is measured based on the LaBr_3(Ce) detector under different parameters. The results show that the larger the shaping parameters(m and k), the closer the shaping result is to Gaussian shape, the wider is the shaped pulse, the higher is the energy resolution, and the lower is the pulse count rate. For the same batch of pulse signals, the energy resolution is increased from 3.8 to 3.5%, and the full energy peak area is reduced from 7815 to 6503. Thus, the optimal shaping parameters are m=3 and k=0:95. These research results can provide a design reference for the development of digital nuclear spectrometer measurement systems.
引文
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18.J.B.Zhou,X.Hong,R.B.Wang et al.,Study of recursive model for pole-zero cancellation circuit.Nucl.Sci.Tech.25(1),38-42(2014).https://doi.org/10.13538/j.1001-8042/nst.25.010403
19.Y.Y.Liu,J.L.Zhang,R.Zhou et al.,Digitalization of CR-RCm filter.Nucl.Tech.40(6),44-48(2017).https://doi.org/10.11889/j.0253-3219.2017.hjs.40.060403.(in Chinese)
20.Y.Y.Liu,J.L.Zhang,L.F.Liu et al.,Implementation of real-time digital CR-RCm shaping filter on FPGA for gamma-ray spectroscopy.Nucl.Instrum.Methods Phys.Res.Sect.A 906,1-9(2018).https://doi.org/10.1016/j.nima.2018.05.020
21.M.Nakhostin,Recursive algorithms for real-time digital CR-(RC)n pulse shaping.IEEE Trans.Nucl.Sci.58(5),2378-2381(2011).https://doi.org/10.1109/TNS.2011.2164556
22.H.Q.Zhang,W.H.Lu,B.Tang et al.,Methods of pulse pile-up identification in digital nuclear spectrometer system.J.East China Inst.Technol.Nat.Sci.35(3),281-284(2012).https://doi.org/10.3969/j.issn.1674-3504.2012.03.013.(in Chinese)
23.H.Q.Zhang,L.Q.Ge,B.Tang et al.,Optimal choice of trapezoidal shaping parameters in digital nuclear spectrometer system.Nucl.Sci.Tech.24(6),109-114(2013).https://doi.org/10.13538/j.1001-8042/nst.2013.06.011
2.B.Gan,T.C.Wei,W.Gao et al.,Design and performances of a low-noise and radiation-hardened readout ASIC for CdZnTe detectors.J.Semicond.37(6),177-183(2016).https://doi.org/10.1088/1674-4926/37/6/065007
3.W.T.Evariste,S.Hong,Q.Yi et al.,Design and simulation of Gaussian shaping amplifier made only with CMOS FET for FEEof particle detector.Nucl.Sci.Tech.21(5),312-315(2010).https://doi.org/10.13538/j.1001-8042/nst.21.312-315
4.V.T.Jordanov,G.F.Knoll,A.C.Huber et al.,Digital techniques for real-time pulse shaping in radiation measurements.Nucl.Instrum.Methods A 353,261-264(1994).https://doi.org/10.1016/0168-9002(94)91652-7
5.A.Pullia,A.Geraci,G.Ripamonti,Quasi-optimum c and Xspectroscopy based on real-time digital techniques.Nucl.Instrum.Methods A 439,378-384(2000).https://doi.org/10.1016/S0168-9002(99)00897-9
6.C.Imperiale,A.Imperiale,On nuclear spectrometry pulses digital shaping and processing.Measurement 30,49-73(2001).https://doi.org/10.1016/S0263-2241(00)00057-9
7.N.Menaa,P.D.Agostino,B.Zakrzewski et al.,Evaluation of real-time digital pulse shapers with various HPGe and silicon radiation detectors.Nucl.Instrum.Methods A 652,512-515(2011).https://doi.org/10.1016/j.nima.2010.08.095
8.V.T.Jordanov,Exponential signal synthesis in digital pulse processing.Nucl.Instrum.Methods A 670,18-24(2012).https://doi.org/10.1016/j.nima.2011.12.042
9.A.Regadio,S.Sanchez-Prieto,M.Prieto et al.,Implementation of a real-time adaptive digital shaping for nuclear spectroscopy.Nucl.Instrum.Methods A 735,297-303(2014).https://doi.org/10.1016/j.nima.2013.09.063
10.V.T.Jordanov,Unfolding-synthesis technique for digital pulse processing.Part 1:unfolding.Nuclear Instrum.Methods Phys.Res.A 805,63-71(2016).https://doi.org/10.1016/j.nima.2015.07.040
11.G.Zeng,J.Yang,T.Hu et al.,Baseline restoration technique based on symmetrical zero-area trapezoidal pulse shaper.Nucl.Instrum.Methods A 858,57-61(2017).https://doi.org/10.1016/j.nima.2017.03.049
12.Q.Ge,L.Q.Ge,H.W.Yuan et al.,A new digital Gaussian pulse shaping algorithm based on bilinear transformation.Nucl.Sci.Tech.26(1),37-41(2015).https://doi.org/10.13538/j.1001-8042/nst.26.010402
13.H.Q.Huang,X.F.Yang,W.C.Ding et al.,Estimation method for parameters of overlapping nuclear pulse signal.Nucl.Sci.Tech.28(1),57-64(2017).https://doi.org/10.1007/s41365-016-0161-z
14.X.Hong,Y.J.Ma,J.B.Zhou et al.,New methods to remove baseline drift in trapezoidal pulse shaping.Nucl.Sci.Tech.26(5),58-62(2015).https://doi.org/10.13538/j.1001-8042/nst.26.050402
15.X.Hong,S.J.Ni,J.B.Zhou et al.,Simulation study on Gaussian pulse shaping algorithm.Nucl.Tech.39(11),49-54(2016).https://doi.org/10.11889/j.0253-3219.2016.hjs.39.110403.(in Chinese)
16.J.B.Zhou,W.Zhou,J.R.Lei et al.,Study of time-domain digital pulse shaping algorithms for nuclear signals.Nucl.Sci.Tech.23(3),150-155(2012).https://doi.org/10.13538/j.1001-8042/nst.23.150-155
17.Q.Ge,L.Q.Ge,X.L.Li,Research on digital shaping algorithm of the nuclear signal based on the same impulse response.Nucl.Electron.Detect.Technol.34(8),942-944(2014).https://doi.org/10.3969/j.issn.0258-0934.2014.08.006.(in Chinese)
18.J.B.Zhou,X.Hong,R.B.Wang et al.,Study of recursive model for pole-zero cancellation circuit.Nucl.Sci.Tech.25(1),38-42(2014).https://doi.org/10.13538/j.1001-8042/nst.25.010403
19.Y.Y.Liu,J.L.Zhang,R.Zhou et al.,Digitalization of CR-RCm filter.Nucl.Tech.40(6),44-48(2017).https://doi.org/10.11889/j.0253-3219.2017.hjs.40.060403.(in Chinese)
20.Y.Y.Liu,J.L.Zhang,L.F.Liu et al.,Implementation of real-time digital CR-RCm shaping filter on FPGA for gamma-ray spectroscopy.Nucl.Instrum.Methods Phys.Res.Sect.A 906,1-9(2018).https://doi.org/10.1016/j.nima.2018.05.020
21.M.Nakhostin,Recursive algorithms for real-time digital CR-(RC)n pulse shaping.IEEE Trans.Nucl.Sci.58(5),2378-2381(2011).https://doi.org/10.1109/TNS.2011.2164556
22.H.Q.Zhang,W.H.Lu,B.Tang et al.,Methods of pulse pile-up identification in digital nuclear spectrometer system.J.East China Inst.Technol.Nat.Sci.35(3),281-284(2012).https://doi.org/10.3969/j.issn.1674-3504.2012.03.013.(in Chinese)
23.H.Q.Zhang,L.Q.Ge,B.Tang et al.,Optimal choice of trapezoidal shaping parameters in digital nuclear spectrometer system.Nucl.Sci.Tech.24(6),109-114(2013).https://doi.org/10.13538/j.1001-8042/nst.2013.06.011