Advertisement

A Reconfigurable Virtual Nuclear Pulse Generator via the Inversion Method

  • Weigang Yin
  • Lian Chen
  • Feng Li
  • Baochen Wang
  • Zhou He
  • Ge Jin
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 212)

Abstract

In this paper, we present a design of reconfigurable virtual nuclear pulse generator which can simulate the nuclear pulse signals. First, the inversion method is used to generate random numbers (amplitudes of pulses and time intervals between adjacent pulses) which indicate the statistical characteristics of the real nuclear pulses in amplitude and time. Then, the digital pulses are synthesized in FPGA using these amplitudes and time intervals. Finally, a DAC is used to output the emulated nuclear signal. Through this design, the emulated signal can be the same as the real nuclear pulses to obey a specific energy distribution in amplitude and subject to the Poisson distribution in time. Compared with commercial periodic signal generators, it can better test the performances of the nuclear spectrometers especially the throughput, pile-up rejection and baseline recovery capability. Experimental results show that the generator behaves like the real radiation source and the detector very well, and it has a count rate beyond 2 M/s.

Keywords

Nuclear pulse generator Inversion method Random numbers 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China under Grant No. 11375179.

References

  1. 1.
    Wiernik, M.: Normal and random pulse generators for the correction of dead-time losses in nuclear spectrometry. Nucl. Instrum. Methods 96, 325–329 (1971)ADSCrossRefGoogle Scholar
  2. 2.
    Veiga, A., Spinelli, E.: A pulse generator with poisson-exponential distribution for emulation of radioactive decay events. In: VII Latin American Symposium on Circuits and Systems (LASCAS), pp. 31–34 (2016)Google Scholar
  3. 3.
    Cheung, R.C.C., Lee, D.U., Luk, W., Villasenor, J.D.: Hardware generation of arbitrary random number distributions from uniform distributions via the inversion method. IEEE Trans. Very Large Scale Integr. Syst. 15, 952–962 (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Weigang Yin
    • 1
  • Lian Chen
    • 1
  • Feng Li
    • 1
  • Baochen Wang
    • 1
  • Zhou He
    • 1
  • Ge Jin
    • 1
  1. 1.State Key Laboratory of Particle Detection and ElectronicsUSTCHefeiChina

Personalised recommendations