Observation probability estimation of dead-time models using Monte Carlo simulations

• Autorzy: Arkani Mohammad

Identyfikatory

DOI

10.24425/mms.2021.136614

• Języki publikacji: EN

Abstrakty

EN

One of difficulties of working with pulse mode detectors is dead time and its distorting effect on measuring with the random process. Three different models for description of dead time effect are given, these are paralizable, non-paralizable, and hybrid models. The first two models describe the behaviour of the detector with one degree of freedom. But the third one which is a combination of the other two models, with two degrees of freedom, proposes a more realistic description of the detector behaviour. Each model has its specific observation probability. In this research, these models are simulated using the Monte Carlo method and their individual observation probabilities are determined and compared with each other. The Monte Carlo simulation, is first validated by analytical formulas of the models and then is utilized for calculation of the observation probability. Using the results, the probability for observing pulses with different time intervals in the output of the detector is determined. Therefore, it is possible by comparing the observation probability of these models with the experimental result to determine the proper model and optimized values of its parameters. The results presented in this paper can be applied to other pulse mode detection and measuring systems of physical stochastic processes.

Słowa kluczowe

EN

time interval between pulses; nuclear detector; Monte Carlo simulation; dead time model

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2021
• Tom: Vol. 28, nr 2
• Strony: 383--395
• Opis fizyczny: Bibliogr. 15 poz., rys., wykr., wzory

Twórcy

autor

Arkani Mohammad

• Nuclear Science & Technology Research Institute (NSRTI), Tehran, Iran. P.O. Box: 143995-1113

Bibliografia

• [1] Arkani, M., & Raisali, G. (2015). Measurement of dead time by time interval distribution method. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 774, 151-158. https://doi.org/10.1016/j.nima.2014.11.069
• [2] Knoll, G. F. (1999). Radiation detection and measurement. John Wiley & Sons.
• [3] Lee, S. H., & Gardner, R. P. (2000). A new G-M counter dead time model. Applied Radiation and Isotopes, 53(4-5), 731-737. https://doi.org/10.1016/S0969-8043(00)00261-X
• [4] Yousaf, M., Akyurek, T., & Usman, S. (2015). A comparison of traditional and hybrid radiation detector dead-time models and detector behavior. Progress in Nuclear Energy, 83, 177-185. https://doi.org/10.1016/j.pnucene.2015.03.018
• [5] Arkani, M., & Khalafi, H. (2013). An improved formula for dead time correction of GM detectors. Nukleonika, 58(4), 533-536.
• [6] Arkani, M., & Khalafi, A. (2013). Efficient dead time correction of GM counters using feed forward artificial neural network. Nukleonika, 58(2), 317-321.
• [7] Gilad, E., Dubi, C., Geslot, B., Blaise, P., & Kolin, A. (2017). Dead time corrections using the backward extrapolation method. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 854, 53-60. https://doi.org/10.1016/j.nima.2017.02.026
• [8] Chatterji, S., Dennis, G., Helsby, W. I., & Tartoni, N. (2019). Monte Carlo simulation of dead time in fluorescence detectors and its dependence on beam structure. IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), United Kingdom, 1-5. https://doi.org/10.1109/NSS/MIC42101.2019.9059789
• [9] Cao, Y., Gohar, Y., & Talamo, A. (2017). Monte Carlo Studies of the Neutron Detector Dead Time Effects on Pulsed Neutron Experiments. International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Korea.
• [10] Lee S. H., Jae, M., & Gardner, R. P. (2007). Non-Poisson counting statistics of a hybrid G-M counter dead time model. Nuclear Instruments and Methods in Physics Research B, 263, 46-49. https://doi.org/10.1016/j.nimb.2007.04.041
• [11] Robinson, D. (2019). Monte Carlo Simulation Modeling Techniques to Measure & Understand Instrument Dead Time in PET Images. SM Journal of Clinical and Medical Imaging, 5(4).
• [12] Ida, T. (2007). Monte Carlo simulation of the effect of counting losses on measured X-ray intensities. Journal of Applied Crystallography, 40(5), 964-965. https://doi.org/10.1107/S002188980703854X
• [13] Vincent, C. H., (1973). Random Pulse Trains: Their Measurement and Statistical Properties, Peter Peregrinus Ltd.
• [14] Mathworks (2018). MATLAB Reference Guide. The Math Works Inc.
• [15] Arkani, M. (2015). A high performance digital time interval spectrometer: an embedded, FPGA-based system with reduced dead time behaviour. Metrology and Measurement Systems, 22(4), 601-619. https://doi.org/10.1515/mms-2015-0048

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).