Investigation of the performance of Superheated Drop Detectors for unfolding fast neutron spectra produced by cosmic rays using an Adaptive Network-based Fuzzy Inference System (ANFIS)

Badiei, Sh.; Rezaeian, P.

doi:10.24200/nst.2021.974.1660

Document Type : Research Paper

Authors

Radiation Application Research School, Nuclear Science and Technology Research Institute, P. O. Box 11365-3486, Tehran - Iran

https://doi.org/10.24200/nst.2021.974.1660

Abstract

In this paper, the fast neutron spectra at altitudes of 3 and 5 km were unfolded by the response of Superheated Drop Detectors and using an Adaptive Network-based Fuzzy Inference System (ANFIS). ANFIS is a Takagi-Sugeno Fuzzy Inference System implemented in the framework of adaptive networks. This tool works similarly to human thinking in dealing with uncertain and erroneous problems. The response matrix of five Superheated Drop Detectors under various external pressures was calculated by an application developed using the Geant4 simulation toolkit and was used to obtain inputs of ANFIS. Also, the neutron spectra of the IAEA technical reports were utilized as the targets. The reference spectra were unfolded with RMSEs of 0.005 and 0.011. The relative agreement between the unfolded and reference spectra shows that these detectors and ANFIS can be used as a new technique for unfolding neutron spectra produced by cosmic radiations in the atmosphere.

Keywords

20.1001.1.17351871.1402.44.1.8.8

References

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14. S. Badiei, et al, Development and validation of a Geant4 application to calculate the response matrix of a set of superheated drop detectors under various external pressures, Nucl. Instrum. Meth. Phys. Res. A. 939 (2019) 55.

15. S. Badiei, et al, Development and experimental validation of a of a fast neutron spectrometry system based on superheated drop detectors (SDDs) operating under different external pressures, Nucl. Instrum. Meth. Phys. Res. A. 1010 (2021).

16. S. Agostinelli, et al., Geant4 a simulation toolkit, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 506, 250 (2003).

17. J.C. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clustering algorithm, Computers and Geosciences, 10, 191 (1984).

18. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum Press, (1981).

19. S. Chiu, Fuzzy Model Identification Based on Cluster Estimation, Journal of Intelligent & Fuzzy Systems, 2, 267 (1994)

Journal of Nuclear Science, Engineering and Technology (JONSAT)

Investigation of the performance of Superheated Drop Detectors for unfolding fast neutron spectra produced by cosmic rays using an Adaptive Network-based Fuzzy Inference System (ANFIS)

References

References

1. T. Nakamura, Cosmic-ray Neutron Spectrometry and Dosimetry, Journal of Nuclear Sciemce and Technology Supplement, 5, 1-7 (2008).

2. M. Gatu Johnson, et al, The 2.5 MeV neutron time-of-flight spectrometer TOFOR for experiments at JET, Nuclear Instruments and Methods in Physics Research Section A, 591(2), 417 (2008).

3. J.R.D. Copley, T.J. Udovic, J. Res, Neutron Time-of-Flight Spectroscopy, Natl. Inst. Stand. Technol, 98, 71 (1993).

4. R. Ciolini, et al, A feasibility study of a SiC sandwich neutron spectrometer, Radiation Measurements, 46(12), 1634 (2011).

5. P. Rezaeian, et al, Development of a new pressure dependent threshold superheated drop detector for neutrons, Nuclear Instruments and Methods in Physics Research Section A, 776, 50 (2015).

6. J.C. McDonald, B.R.L. Siebert, W.G. Alberts, Neutron spectrometry for radiation protection purposes, Nuclear Instruments and Methods in Physics Research A, 476(1-2), 347–352 (2002).

7. H.R. Vega-Carrillo, et al., Neutron spectrometry using artificial neural networks, Radiation Measurements, 41, 425-431 (2006).

8. S. Badiei, et al, Unfolding of fast neutron spectra by superheated drop detectors using Adaptive Network-Based Fuzzy Inference System (ANFIS), Nucl. Instrum. Meth. Phys. Res. A. 944, 162517 (2019).

9. K. O’Brien, et al, Atmospheric cosmic rays and solar energetic particles at aircraft altitudes, Environ. Int. 22 Suppl. 18–44 (1996).

11. J. Shing, R. Jang, ANFIS Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, 23, 665 (1993).

12. R. Griffith, J. Palfalvi, U. Madhvantah, Compendium of Neutron Spectra and Detector Responses for Radiation Protection Purposes, IAEA, Technical Report Series, No. 403 ( 2001).

13. R. Griffith, J. Palfalvi, U. Madhvantah, Compendium of Neutron Spectra and Detector Responses for Radiation Protection Purposes, IAEA, Technical Report Series, No. 318 ( 1990).

14. S. Badiei, et al, Development and validation of a Geant4 application to calculate the response matrix of a set of superheated drop detectors under various external pressures, Nucl. Instrum. Meth. Phys. Res. A. 939 (2019) 55.

15. S. Badiei, et al, Development and experimental validation of a of a fast neutron spectrometry system based on superheated drop detectors (SDDs) operating under different external pressures, Nucl. Instrum. Meth. Phys. Res. A. 1010 (2021).

16. S. Agostinelli, et al., Geant4 a simulation toolkit, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 506, 250 (2003).

17. J.C. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clustering algorithm, Computers and Geosciences, 10, 191 (1984).

18. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum Press, (1981).

Volume 44, Issue 1 - Serial Number 103
April 2023
Pages 58-66

Investigation of the performance of Superheated Drop Detectors for unfolding fast neutron spectra produced by cosmic rays using an Adaptive Network-based Fuzzy Inference System (ANFIS)

References

References

1. T. Nakamura, Cosmic-ray Neutron Spectrometry and Dosimetry, Journal of Nuclear Sciemce and Technology Supplement, 5, 1-7 (2008).

2. M. Gatu Johnson, et al, The 2.5 MeV neutron time-of-flight spectrometer TOFOR for experiments at JET, Nuclear Instruments and Methods in Physics Research Section A, 591(2), 417 (2008).

3. J.R.D. Copley, T.J. Udovic, J. Res, Neutron Time-of-Flight Spectroscopy, Natl. Inst. Stand. Technol, 98, 71 (1993).

4. R. Ciolini, et al, A feasibility study of a SiC sandwich neutron spectrometer, Radiation Measurements, 46(12), 1634 (2011).

5. P. Rezaeian, et al, Development of a new pressure dependent threshold superheated drop detector for neutrons, Nuclear Instruments and Methods in Physics Research Section A, 776, 50 (2015).

6. J.C. McDonald, B.R.L. Siebert, W.G. Alberts, Neutron spectrometry for radiation protection purposes, Nuclear Instruments and Methods in Physics Research A, 476(1-2), 347–352 (2002).

7. H.R. Vega-Carrillo, et al., Neutron spectrometry using artificial neural networks, Radiation Measurements, 41, 425-431 (2006).

8. S. Badiei, et al, Unfolding of fast neutron spectra by superheated drop detectors using Adaptive Network-Based Fuzzy Inference System (ANFIS), Nucl. Instrum. Meth. Phys. Res. A. 944, 162517 (2019).

9. K. O’Brien, et al, Atmospheric cosmic rays and solar energetic particles at aircraft altitudes, Environ. Int. 22 Suppl. 18–44 (1996).

11. J. Shing, R. Jang, ANFIS Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, 23, 665 (1993).

12. R. Griffith, J. Palfalvi, U. Madhvantah, Compendium of Neutron Spectra and Detector Responses for Radiation Protection Purposes, IAEA, Technical Report Series, No. 403 ( 2001).

13. R. Griffith, J. Palfalvi, U. Madhvantah, Compendium of Neutron Spectra and Detector Responses for Radiation Protection Purposes, IAEA, Technical Report Series, No. 318 ( 1990).

14. S. Badiei, et al, Development and validation of a Geant4 application to calculate the response matrix of a set of superheated drop detectors under various external pressures, Nucl. Instrum. Meth. Phys. Res. A. 939 (2019) 55.

15. S. Badiei, et al, Development and experimental validation of a of a fast neutron spectrometry system based on superheated drop detectors (SDDs) operating under different external pressures, Nucl. Instrum. Meth. Phys. Res. A. 1010 (2021).

16. S. Agostinelli, et al., Geant4 a simulation toolkit, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 506, 250 (2003).

17. J.C. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clustering algorithm, Computers and Geosciences, 10, 191 (1984).

18. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum Press, (1981).

Volume 44, Issue 1 - Serial Number 103April 2023Pages 58-66

Volume 44, Issue 1 - Serial Number 103
April 2023
Pages 58-66