Skip to main content
SpringerLink
Log in

Pulse Pile-up Correction by Particle Swarm Optimization with Double-layer Parameter Identification Model in X-ray Spectroscopy

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In X-ray spectrum analysis, the pulse pile-up is a long-standing issue which deteriorates the energy resolution and count rates of the radiation detection systems. In this study, a novel pulse pile-up identification method based on particle swarm optimization and double-layer parameter identification model (PSO-DLPIM) is proposed. Different Gaussian pile-up waveforms are realized by exponential pulse through Sallen-Key (S-K) low-pass filtering. Then, the proposed model recognizes the parameters of each sub-Gaussian pulse. Especially, it can be used to modelling the pulse indirectly without a certain model parameter and overcomes the model mismatch troubles. Finally, computer simulations and experimental tests are carried out and the results show that this method has higher accuracy for the recognition of pile-up pulses. The example shows that the minimum distance between pulses that can be identified by this method is 0.05 μs. And when the pulse generation time is known and the environmental noise is low, the relative error of the amplitude of pulse pile-up recognition is as low as 0.15%. Therefore, this method can greatly improve the resolution of the X-ray spectrum.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Yang, X., et al. (2017). Application of Particle Swarm Algorithm and GMM-SDR Model in Overlapping Spectrum Peak Analysis. Spectroscopy and Spectral Analysis.

  2. Zeng, G. Q., et al. (2014). Application of the Racial Algorithm in Energy Dispersive X-Ray Fluorescence Overlapped Spectrum Analysis. Guang pu xue yu guang pu fen xi = Guang pu 34(2): p. 562–564 (in Chinese).

  3. Gerardi, G., et al. (2007). Digital filtering and analysis for a semiconductor X-ray detector data acquisition. Nuclear Instruments & Methods in Physics Research, 571(1–2), 378–380.

    Article  Google Scholar 

  4. Trigano, T., et al. (2007). Statistical Pileup Correction Method for HPGe Detectors. IEEE Transactions on Signal Processing, 55(10), 4871–4881.

    Article  MathSciNet  Google Scholar 

  5. Valentin, T., & Jordanov. (2003). Real time digital pulse shaper with variable weighting function. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 505(1–2): p. 347–351.

  6. Wang, X., et al. (2020). An effective digital pulse processing method for pile-up pulses in decay studies of short-lived nuclei. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 971: p. 164068.

  7. Yu, J., et al. (2020). Pile-up pulse continuous zone reject method. Applied Radiation and Isotopes 165: p. 109319.

  8. Wulf, D., et al. (2016). Technique for Recovering Pile-Up Events from Microcalorimeter Data. Journal of Low Temperature Physics, 184(1–2), 431–435.

    Article  Google Scholar 

  9. Nakhostin, M. (2020). A technique for the reduction of pulse pile-up effect in pulse-shape discrimination of organic scintillation detectors. Nuclear Engineering and Technology, 52(2), 360–365.

    Article  Google Scholar 

  10. Du, S., et al. (2020). Identification and correction method of nuclear detector pulse forming pile-up. Energy Reports, 6, 343–351.

    Article  Google Scholar 

  11. Sabbatucci, L., Scot, V., & Fernandez, J. E. (2014). Multi-shape pulse pile-up correction: The MCPPU code. Radiation Physics and Chemistry, 104, 372–375.

    Article  Google Scholar 

  12. Kafaee, M., Zarandi, A. M., & Taheri, A. (2016). Neural-based pile-up correction and ballistic deficit correction of X-ray semiconductor detectors using the Monte Carlo simulation and the Ramo theorem. Radiation effects and defects in solids 171(3–4): p. 271–278.

  13. Mendoza, E., et al. (2014). Pulse pile-up and dead time corrections for digitized signals from a BaF 2 calorimeter. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 768, 55–61.

    Article  Google Scholar 

  14. Luo, X. L., et al. (2018). Pulse pile-up identification and reconstruction for liquid scintillator based neutron detectors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 897, 59–65.

    Article  Google Scholar 

  15. Qin, Z., et al. (2018). A pulse-shape discrimination method for improving Gamma-ray spectrometry based on a new digital shaping filter. Radiation Physics and Chemistry, 145, 193–201.

    Article  Google Scholar 

  16. Ferri, E., et al. (2016). Pile-Up Discrimination Algorithms for the HOLMES Experiment. Journal of Low Temperature Physics, 184(1–2), 405–411.

    Article  Google Scholar 

  17. Kafaee, M., & Moussavi-Zarandi, A. (2016). Baseline restoration and pile-up correction based on bipolar cusp-like shaping for high-resolution radiation spectroscopy. Journal of the Korean Physical Society, 68(8), 960–964.

    Article  Google Scholar 

  18. Hong, X., et al. (2016). Study on the relationship between the shaping parameters of trapezoidal pulse shaping algorithm and the trapezoidal pulse shape. Nuclear Electronics & Detection Technology.

  19. Ren, Y. Q., et al. (2018). The Simulation and Evaluation for Method of Gaussian and Trapezoidal Digital Shaping of Nuclear Signal. Hedianzixue Yu Tance Jishu/Nuclear Electronics & Detection Technology, 38(1), 105–110.

    Google Scholar 

  20. Jiri, et al. (2016). Software emulator of nuclear pulse generation with different pulse shapes and pile-up. Nuclear Instruments & Methods in Physics Research.

  21. Zhou, J., et al. (2012). Study of time-domain digital pulse shaping algorithms for nuclear signals. Nuclear Science and Techniques.

  22. Jordanov, V. T. (2012). Exponential signal synthesis in digital pulse processing. Nuclear Instruments & Methods in Physics Research.

  23. Roessl, E., Daerr, H., & Proksa, R. (2016). A Fourier approach to pulse pile-up in photon-counting x-ray detectors. Medical physics (Lancaster), 43(3), 1295–1298.

    Google Scholar 

  24. Sabbatucci, L., & Fernández, J. E. (2017). First principles pulse pile-up balance equation and fast deterministic solution. Radiation Physics and Chemistry, 137, 12–17.

    Article  Google Scholar 

  25. Lee, D., et al. (2017). A new cross-detection method for improved energy-resolving photon counting under pulse pile-up. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 867, 154–162.

    Article  Google Scholar 

  26. Lee, D., et al. (2017). Energy-correction photon counting pixel for photon energy extraction under pulse pile-up. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 856, 36–46.

    Article  Google Scholar 

  27. Acconcia, G., et al. (2018). Fast fully-integrated front-end circuit to overcome pile-up limits in time-correlated single photon counting with single photon avalanche diodes. Optics Express, 26(12), 15398.

    Article  Google Scholar 

  28. O'Connor, P., et al. (1998) Ultra Low Noise CMOS preamplifier-shaper for X-ray spectroscopy. Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment. 409(1–3): p. 315–321.

  29. A, J. L., et al. (2013). Real-time evolvable pulse shaper for radiation measurements. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 727(3): p. 73–83.

  30. Menaa, N., et al. (2011). Evaluation of real-time digital pulse shapers with various HPGe and silicon radiation detectors. Nuclear Inst & Methods in Physics Research A, 652(1), 512–515.

    Article  Google Scholar 

  31. Hong, X., et al. (2018). Counting-loss correction for X-ray spectroscopy using unit impulse pulse shaping. Journal of Synchrotron Radiation, 25(2), 505–513.

    Article  Google Scholar 

  32. Zeng, G. Q., et al. (2017). Digital Fast Shaping Algorithm for Spectrum Readout of Slow Decay Scintillator at High Count Rate. Atomic Energy Science and Technology, 51(9), 1671–1677.

    Google Scholar 

  33. Mohammadian-Behbahani, M. R., & Saramad, S. (2020). A comparison study of the pile-up correction algorithms. Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment 951(Jan.21): p. 163013.1–163013.10.

  34. Zhou, J. B., et al. (2015). Trapezoidal pulse shaping for pile-up pulse identification in X-ray spectrometry. Chinese Physics C. 06(v.39): p. 112–117.

  35. Xiao, W., et al. (2015). Model-based pulse deconvolution method for NaI(Tl) detectors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 769, 5–8.

    Article  Google Scholar 

  36. Wen, X., & Yang, H. (2015). Study on a digital pulse processing algorithm based on template-matching for high-throughput spectroscopy. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 784, 269–273.

    Article  Google Scholar 

  37. Chen Shi-Guo. (2005). Design and Realization of The Gaussian Shaping Filtering in Digital Nuclear Instrument System (Ph. D. Thesis). Sichuan: Sichuan University (in Chinese).

  38. Ruan-Yu, Z. (2006). Research on Digital Nuclear Energy Spectrum Acquisition System (Ph. D. Thesis). Sichuan: Sichuan University (in Chinese).

  39. Li, M., Chen, H., Shi, X., et al. (2019). A multi-information fusion "triple variables with iteration" inertia weight PSO algorithm and its application. Applied Soft Computing 84:105677.

  40. Liu, J., Ma, X., Li, X., et al. (2020). Random Convergence Analysis of Particle Swarm Optimization Algorithm with Time-varying Attractor. Swarm and Evolutionary Computation 61(2):100819.

  41. Shehata, R. H., et al. (2014). Particle Swarm Optimization: Developments and Application Fields. International Journal on Power Engineering and Energy.

Download references

Acknowledgements

This work was partially supported by the National Key R&D Program of China under Grant 2017YFC0602100, partially supported by Major science and technology projects of Sichuan Province China under Grant 2020ZDZX0007, and partially supported by Technology Planning Project of Sichuan Province China under Grant 2021YJ0325.

Author information

Authors and Affiliations

  1. Applied Nuclear Techniques in Geosciences Key Laboratory, Chengdu University of Technology, Chengdu, 610059, China

    Yang Xiao-feng, Huang Hong-Quan, Zeng Guo-Qiang, Ge Liang-Quan, Jiang Kai-ming, Gu Min, Hu Chuan-Hao & Lai Mao-Lin

Authors
  1. Yang Xiao-feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huang Hong-Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeng Guo-Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ge Liang-Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiang Kai-ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gu Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hu Chuan-Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lai Mao-Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huang Hong-Quan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao-feng, Y., Hong-Quan, H., Guo-Qiang, Z. et al. Pulse Pile-up Correction by Particle Swarm Optimization with Double-layer Parameter Identification Model in X-ray Spectroscopy. J Sign Process Syst 94, 377–386 (2022). https://doi.org/10.1007/s11265-021-01698-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-021-01698-4

Keywords

Search

Navigation