DOI QR코드

DOI QR Code

High rate diffusion-scale approximation for counters with extendable dead time

  • Dubi, Chen (Physics Department, Nuclear Research Center of the Negev) ;
  • Atar, Rami (Viterbi Faculty of Electrical Engineering, Technion-Israel Institute of Technology)
  • Received : 2018.12.12
  • Accepted : 2019.04.16
  • Published : 2019.09.25

Abstract

Measuring occurrence times of random events, aimed to determine the statistical properties of the governing stochastic process, is a basic topic in science and engineering, and has been the subject of numerous mathematical modeling approaches. Often, true statistical properties deviate from measured properties due to the so called dead time phenomenon, where for a certain time period following detection, the detection system is not operational. Understanding the dead time effect is especially important in radiation measurements, often characterized by high count rates and a non-reducible detector dead time (originating in the physics of particle detection). The effect of dead time can be interpreted as a suitable rarefied sequence of the original time sequence. This paper provides a limit theorem for a high rate (diffusion-scale) counter with extendable (Type II) dead time, where the underlying counting process is a renewal process with finite second moment for the inter-event distribution. The results are very general, in the sense that they refer to a general inter arrival time and a random dead time with general distribution. Following the theoretical results, we will demonstrate the applicability of the results in three applications: serially connected components, multiplicity counting and measurements of aerosol spatial distribution.

Keywords

References

  1. L. Takacs, On a probability problem arising in the theory of counters, Math. Proc. Camb. Phil. Soc. 52 (1956) 488-498. https://doi.org/10.1017/S0305004100031480
  2. P. Billingsley, Convergence of Probability Measures, John Wiley & Sons, 2013.
  3. G.F. Knoll, Radiation Detection and Measurement, John Wiley and Sons, Inc., 2000.
  4. L. Pal, I. Pazsit, On some problems in the counting statistics of nuclear particles investigation of the dead time problems, Nucl. Instrum. Methods 693 (2012) 26-50. https://doi.org/10.1016/j.nima.2012.07.036
  5. J.W. Muller, Dead time problems, Nucl. Instrum. Methods 112 (1973) 47-57. https://doi.org/10.1016/0029-554X(73)90773-8
  6. J.E. Normey-Rico, E.F. Camacho, Dead-time compensators, A Surv. Contr. Eng. Pract. 16 (2008) 407-428. https://doi.org/10.1016/j.conengprac.2007.05.006
  7. S. Jeong, M. Park, The analysis and compensation of dead-time effects in PWM inverters, IEEE Trans. Ind. Electron. 38 (1991) 108-114. https://doi.org/10.1109/41.88903
  8. T. J Spinks, T. Jones, M. C Gilardi, J.D. Heather, Physical performance of the latest generation of commercial positron scanner, IEEE Trans. Nucl. Sci. 35 (1988) 721-725. https://doi.org/10.1109/23.12819
  9. S.M.Nelms, C.R.Quetel, T. Prohaska, J.Vogl, P.D.Taylor, Evaluationofdetectordead time calculation models for ICP-MS, J. Anal. At. Spectrom. 16 (2001) 333-338. https://doi.org/10.1039/B007913H
  10. W. Feller, On Probability Problems in the Theory of Counters. William Feller: Selected Papers, Springer, 2015, pp. 751-759.
  11. M.J. Hammersley, On counters with random dead time, I. Math. Proc. Camb. Phil. Soc. 49 (1953) 623-627. https://doi.org/10.1017/S0305004100028826
  12. Ronald Pyke on renewal processes related to type I and type II counter models, Ann. Math. Stat. 29 (1958) 737-754. https://doi.org/10.1214/aoms/1177706533
  13. Y. Kitamura, M. Fukushima, Correction of count-loss effect in neutron correlation methods that employ single neutron counting system for subcriticality measurement, J. Nucl. Sci. Technol. 51 (2014) 766-782. https://doi.org/10.1080/00223131.2014.902779
  14. W. Matthes, R. Haas, Deadtime correction for 'updating deadtime' counters, Ann. Nucl. Energy 12 (1985) 693-698. https://doi.org/10.1016/0306-4549(85)90083-0
  15. M.L. Larsen, Spatial distribution of aerosol particles, in: Investigation of the Poisson assumption Aerosol Science 38, 2007, pp. 807-822. https://doi.org/10.1016/j.jaerosci.2007.06.007
  16. M.L. Larsen, A.B. Kostinski, Simple dead-time corrections for discrete time series of non-Poisson data, Meas. Sci. Technol. 20 (2009).
  17. S. Usman, A. Patil, Radiation detector deadtime and pile up, in: A Review of the status of science Nuclear Engineering and Technology 50, 2018, pp. 1006-1016. https://doi.org/10.1016/j.net.2018.06.014
  18. D. Reilly, N. Ensslin, H. Smith Passive Nondestructive Assay of Nuclear Materials Los Alamos National Laboratory, LA-UR-90-732
  19. S. Croft, L.G. Evans, A. Favalli, D.K. Hauck, D. Henzlova, P. Santi, Revisiting the form of dead time corrections for neutron coincidence counting Radiation, Measurement 47 (2012).
  20. W. Hage, D.M. Cifarelli, Correlation analysis with neutron count distribution for a paralyzing Dead time counter for the assay of spontaneous fissioning materials, Nucl. Sci. Eng. 112 (1992) 136-158. https://doi.org/10.13182/NSE92-A28410
  21. D.K. Hauck, S. Croft, L.G. Evans, A. Favalli, P.A. Santi, J, Dowell Study of a theoretical model for the measured gate moments resulting from correlated detection events and an extending dead time, Nucl. Inst. Method. A 719 (2013) 57-69. https://doi.org/10.1016/j.nima.2013.03.063
  22. S. Croft, S. Cleveland, A. Favalli, R. McElroy, A. Simone, Estimating the effective system dead time parameter for correlated neutron counting, Nucl. Inst. Method. A 871 (2017) 154-160. https://doi.org/10.1016/j.nima.2017.04.042
  23. G. Herdan, Small Particles Statistics, Academic Press, New York, 1960.