بررسی عملکرد آشکارسازهای قطره‌ی فوق‌گرم در بازیابی بیناب نوترون‌های سریع ناشی از پرتوهای کیهانی با روش شبکه‌ی عصبی فازی تطبیقی

بدیعی, شهریار; رضائیان, پیمان

doi:10.24200/nst.2021.974.1660

نوع مقاله : مقاله پژوهشی

نویسندگان

پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، صندوق پستی: 3486-11365، تهران ـ ایران

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

چکیده

در این مقاله بیناب نوترون‌های سریع در ارتفاع‌های 3 و 5 کیلومتری سطح زمین با استفاده از پاسخ آشکارسازهای قطره‌ی فوق گرم و با بهره‌گیری از شبکه‌ی هوشمند عصبی فازی (انفیس) بازیابی شد. انفیس، یک سیستم استنتاج فازی نوع تاکاگی- سوگنو است که در قالب شبکه‌ی تطبیقی پیاده‌سازی شده است. این ابزار مشابه با تفکر انسان در مواجه با مسایل غیرقطعی و همراه با خطا عمل می‌کند. ماتریس پاسخ پنج آشکارساز قطره‌ی فوق‌گرم در فشارهای خارجی مختلف توسط یک برنامه‌ی کاربردی نوشته شده توسط ابزار شبیه‌سازی 4Geant محاسبه گردید و از آن برای محاسبه‌ی ورودی های انفیس استفاده شد. مجموعه بیناب‌های گزارش‌های فنی آژانس بین‌المللی انرژی اتمی نیز به عنوان خروجی به کار گرفته شد. بیناب‌های مرجع با خطاهای 005_/0 و 011_/0 بازیابی شدند. توافق نسبی بین بیناب‌های مرجع و بازیابی شده نشان می‌دهد که این آشکارسازها با کمک انفیس می‌تواند به عنوان یک روش جدید در بازیابی بیناب‌های نوترون ناشی از برهم‌کنش تشعشعات فضایی در جو به کار رود.

کلیدواژه‌ها

عنوان مقاله [English]

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)

نویسندگان [English]

Sh. Badiei
P. Rezaeian

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

چکیده [English]

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.

کلیدواژه‌ها [English]

Geant4
ANFIS
Fast neutron
Superheated drop detector

مراجع

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).

10. H. Schraube, et al., The cosmic ray induced neutron spectrum at the summit of the Zugspitze (2963 m), Radiat. Prot. Dosim., 70, 405–408 (1997)

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).

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

مجله علوم و فنون هسته ای

بررسی عملکرد آشکارسازهای قطره‌ی فوق‌گرم در بازیابی بیناب نوترون‌های سریع ناشی از پرتوهای کیهانی با روش شبکه‌ی عصبی فازی تطبیقی

مراجع

مراجع

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).

دوره 44، شماره 1 - شماره پیاپی 103
فروردین 1402
صفحه 58-66

بررسی عملکرد آشکارسازهای قطره‌ی فوق‌گرم در بازیابی بیناب نوترون‌های سریع ناشی از پرتوهای کیهانی با روش شبکه‌ی عصبی فازی تطبیقی

مراجع

مراجع

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).

دوره 44، شماره 1 - شماره پیاپی 103فروردین 1402صفحه 58-66

دوره 44، شماره 1 - شماره پیاپی 103
فروردین 1402
صفحه 58-66