Pulse shape discrimination of neutrons and gamma rays using kohonen artificial neural networks

• Autorzy: Tambouratzis T. , Chernikova D. , Pzsit I.

Identyfikatory

DOI

10.2478/jaiscr-2014-0006

• Języki publikacji: EN

Abstrakty

EN

The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisation (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n’s) and gamma rays (’s). The effect that (a) the energy level, and (b) the relative size of the training and test sets, have on identification accuracy is also evaluated on the given PSD dataset. The two Kohonen ANNs demonstrate complementary discrimination ability on the training and test sets: while the LVQ is consistently more accurate on classifying the training set, the SOM exhibits higher n/ identification rates when classifying new patterns regardless of the proportion of training and test set patterns at the different energy levels; the average time for decision making equals ˜100 μs in the case of the LVQ and ˜450 μs in the case of the SOM.

Słowa kluczowe

EN

shape; neutron; discrimination; gamma rays; Kohonen artificial neural networks; ANNs; linear vector quantisation; LVQ; self-organizing map; SOM; pulse shape discrimination; PSD

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2013
• Tom: Vol. 3, No. 2
• Strony: 77--88
• Opis fizyczny: Bibliogr. 50 poz., rys.

Twórcy

autor

Tambouratzis T.

• Department of Industrial Management & Technology, University of Piraeus, addressStreet107 Deligiorgi St., CityPiraeus 185 34, country-regionplaceGreece

autor

Chernikova D.

• Department of Industrial Management & Technology, University of Piraeus, addressStreet107 Deligiorgi St., CityPiraeus 185 34, country-regionplaceGreece

autor

Pzsit I.

• Division of Nuclear Engineering, Chalmers University of Technology SE-412 96 CityplaceGothenburg, country-regionSweden

Bibliografia

• [1] R. T. Kouzes, The 3He Supply Problem, Report 413 No. PNNL-18388, Pacific 414 Northwest National Laboratory, Richland, WA, 2009
• [2] A. Enqvist, placeI. Pzsit, S. Avdic, Sample characterization using both neutron and gamma multiplicities, Nuclear Instruments & Methods A, vol. 615, pp. 62-69, 2010
• [3] V. L. Romodanov, V. K. Sakharov, A. G. Belevitin, V. V. Afanas’ev, I. V. Mukhamad’varov, D. N. Chernikov, Computational-experimental studies of a facility for detecting fissile materials in airports, Atomic Energy, vol. 105, pp. 118-123, 2008
• [4] Y. N. Barmakov, E. P. Bogolyubov, O. V. Bochkarev et al., 2011, System of combined active and passive control of fissile materials and their nuclide composition in nuclear wastes, International Journal of Nuclear Energy Science and Technology, vol. 6, pp. 127-135, 2011
• [5] D. Chernikova, V. Romodanov, V. Sakharov, A. Isakova, Analysis of 235U, 239Pu and 241Pu content in a spent fuel assembly using lead slowing down spectrometer and time intervals matrix, Journal of Nuclear Materials Management, vol.40, pp. 9-18, 2012
• [6] D. Chernikova, V. Romodanov, V. Sakharov, Development of the neutron-gamma-neutron (NGN) approach for the fresh and spent fuel assay, the 53rd Annual Meeting of the Institute of Nuclear Materials Management, Orlando, Florida USA, July 2012
• [7] S. A. Pozzi, M. M. Bourne, S. D. Clarke, Pulse shape discrimination in the plastic scintillator EJ-299-33, Nuclear Instruments and Methods in Physics Research Section A, vol. 723, 21 pp. 19-23, 2013
• [8] F. D. Brooks, A scintillation counter with neutron and gamma-ray discriminators, Nuclear Instruments and Methods, vol. 4, pp. 151-163, 1959
• [9] F. T. Kuchnir, F. J. Lynch, Time dependence of scintillations and the effect on pulse-shape discrimination, IEEE Transactions on Nuclear Science, vol. 15, pp. 107 –113, 1968
• [10] P. Sperr, H. Spieler, M. R. Maier, D. Evers, A simple pulse-shape discrimination circuit, Nuclear Instruments and Methods A, vol. 116, pp. 55-59,1974
• [11] S. Marrone, D. Cano-Ott, N. Colonna, C. Domingo, F. Gramegna, E. M. Gonzalez, F. Gunsing, M. Heil, F. Kappeler, Pulse shape analysis of liquid scintillators for neutron studies, Nuclear Instruments and Methods in Physics Research A, vol. 490, pp. 299-307, 2002
• [12] B. D.Mellow, M. D. Aspinall, R. O. Mackin, M. J. Joyce, A. J. Peyton, Digital discrimination of neutrons and gamma-rays in liquid scintillators using pulse gradient analysis, Nuclear Instruments and Methods A, vol. 578, pp. 191-197, 2007
• [13] D. I. Shippen, M.J. Joyce, M. D. Aspinall, A wavelet packet transform inspired method of neutron-gamma discrimination, IEEE Transactions on Nuclear Science, vol. 57, pp. 2617 –2624, 2010
• [14] G. Liu, M. J. Joyce, X. Ma, M. D. Aspinall, A digital method for the discrimination of neutrons and gamma rays with organic scintillation detectors using frequency gradient analysis, IEEE Transactions on Nuclear Science, vol. 57, pp. 1682 –1691, 2010
• [15] D. Wolski, M. Moszynski, T. Ludziejewski, A. Johnson, W. Klamra, O. Skeppstedt, Comparison of n-# discrimination by zero-crossing and digital charge comparison methods, Nuclear Instruments and Methods A, vol. 360, pp. 584-592, 1995
• [16] I. V. Muhamadyarov, Nondestructive testing and detection of fissile and radioactive materials in systems with pulsed neutron sources and digital processing of the experimental data, PhD Dissertation, Russian State Library Electronic Catalogue (OPAC), 2009
• [17] K. A. A. Gamage, M. J. Joyce, N. P. Hawkes,A comparison of four different digital algorithms for pulse-shape discrimination in fast scintillators, Nuclear Instruments and Methods A, vol. 642, pp. 78-83, 2011
• [18] R. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning representations by back propagating errors, Nature, vol. 323, pp. 533-536, 1986
• [19] M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: therprop algorithm, Proceedings of the IEEE Conferenceon Neural Networks, San Francisci, CA, March 28th-April 1st , 1993, pp. 586–591, 1993
• [20] M. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, vol. 6, pp. 525-533, 1993
• [21] Z. Cao, L. F. Miller, M. Buckner, Implementation of dynamic bias for classifiers, Nuclear Instruments and Methods A, vol. 416, pp. 438-445, 1998
• [22] B. Esposito, L. Fortuno, A. Rizo, Neural neutron/gamma discrimination in organic scintillators for fusion applications, Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary, July 25-29, 2004, vol. 4, pp. 2931-2936, 2004
• [23] P. Guazzoni, F. Previdi, S. Russo, M. Sassi, S. M. Saravesi, Pulse shape analysis using subspace identification methods and particle identification using neural networks in CsI(T1) scintillators, Proceedings of the IEEE Nuclear Science Symposium and Medical imaging, Puerto Rico, October 23th -29th, 2005, vol. 3, pp. 1341-1345, 2005
• [24] D. Wisniewski, M. Wisniewska, P. Bruyndonckx, M. Krieguer, S. Tavernier, O. Devroede, C. Lemaitre, J. B. Mosset, C. Morel, Digital pulse shape discrimination methods for phoswich detectors, Proceedings of the IEEE Nuclear Science Symposium and Medical imaging, Puerto Rico, October 23th -29th, 2005, vol. 5, pp. 2979-2983, 2005
• [25] L. Bertalot, B. Esposito, Y. Kascuck, D. Marocco, M. Riva, A. Rizzo, D. Skopintsev, Fast digitizing techniques applied to scintillation detectors, Nuclear Physics B – Proceedings supplement of the Proceedings of the 9th Topical Seminar on innovative Particle and Radiation Detectors, May 23rd-26th, 2004, Siena, Italy, 2004, vol. 150, pp. 78-81, 2006
• [26] J. Gill, T. Persson, K. Sjogren, K. Sols, N. Sundstrom, S. Wranne, Identification of ions by pulseshape analysis and evaluation of Lyso scintillator crystal, Technical Report, Chalmers University of Technology, Goteborg, Sweden, 2008
• [27] G. Liu, M. D. Aspinall, X. Ma, M. J. Joyce, An investigation of the digital discrimination of neutron and # rays with organic scintillation detectors using a artificial neural network, Nuclear Instruments and Methods in Physics Research A, vol. 607, pp. 620-628, 2009
• [28] E. Ronchi, P. A. Soderstrom, J. Nyberg, E. Andersson Sunden, S. Conroy, G. Ericsson, C. Hellesen, M. Gatu Johnson, M. Weiszflog, An artificial neural network based neutron-gamma discrimination and pile-up rejection framework for the BC-501 scintillation detector, Nuclear Instruments and Methods in Physics Research A, vol. 610, pp. 534-539, 2009
• [29] R. Jimenez, M. Sanchez-Raya, J. A. Gomez-Galan, J. L. Flores, J. A. Duenas, I. Martel, Implementation of a neural network for digital pulse shape analysis on FPGA for on-line identification of heavy ions, Nuclear Instruments and Methods in Physics Research A, vol. 674, pp. 99-104, 2012
• [30] N. Yildiz, S. Akkoyun, Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking, Annals of Nuclear Energy, vol. 51, pp. 10-17, 2013
• [31] T. Kohonen, Learning vector quantization for pattern recognition, Report TKK-F-A601, Helsinki University of Technology, Espoo, Finland, 1986
• [32] T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol. 43, no. 1, pp. 59-69, 1982
• [33] MATLAB R2009a, Mathworks, February 2009
• [34] D. Chernikova, K. Axell, I. Pzsit, A. Nordlund, R. Sarwar, A direct method for evaluating the concentration of boric acid in a fuel pool using scintillation detectors for joint-multiplicity measurements, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 714, pp. 90–97, 2013
• [35] T. Tambouratzis, D. Chernikova, I. Pzsit, A comparison of artificial neural network performance: the case of neutron/gamma pulse shape discrimination, 2013 IEEE Symposium Series on Computational Intelligence (CISDA), Singapore, April 16th-19th, 2013, pp. 88-95, 2013
• [36] T. Kohonen, Learning vector quantization, The handbook of brain theory and neural networks (M.A. Arbib, editor), MIT Press, Cambridge, MA, pp. 537–540. 1995
• [37] T. Kohonen, Improved versions of learning vector quantization, Proceedings of the International Joint Conference on Neural Networks, San Diego, California, June 17th, 1990, vol. 1, pp. 545-550, 1990
• [38] T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, K. Torkkola, LVQ PAK: the learning vector quantization programming package, Report A30, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland, 1996
• [39] T. Kohonen, The self-organizing map.Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990
• [40] T. Kohonen, Statistical pattern recognition revisited, Advanced Neural Computers, pp. 137-144, 1990
• 88 T. Tambouratzis, D. Chernikova and I. Pzsit [41] C. Zhu, J. Wang, T. Wang, Analysis of learning vector quantization algorithms for pattern classification, International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, May 9th12th,1995, vol. 5, pp. 34713474, 1995
• [42] M. T. Hagan, H. B. Demuth, M. Beale, Neural Networks Design, PWS Publishing, CO, U.S.A., 1996
• [43] T. Kohonen, SelfOrganizing Maps. Springer, Berlin, 1995
• [44] S. Haykin, Selforganizing maps (chapter 9), in Neural Networks A Comprehensive Foundation (2nd ed.), PrenticeHall, 1999
• [45] A. Ultsch, Emergence in selforganizing feature maps, in H. Ritter, R. Haschke (eds), Proceedings of the 6th International Workshop on SelfOrganizing Maps (WSOM ’07). Bielefeld, Germany, 2007
• [46] S. Kaski, Data Exploration Using SelfOrganizing Maps. PhD thesis, Helsinki University of Technology, Espoo, Finland, 1997
• [47] A. Hmlinen, Selforganizing map and reduced kernel density estimation, PhD thesis, University of Jyvskyl, Jyvskyl, Finland, 1995
• [48] H. ShahHosseini, R. Reza, TASOM: a new time adaptive selforganizing map, IEEE Transactions B: Cybernetics, vol. 33, pp. 271–282, 2003
• [49] D. Alahakoon, S. K. Halgamuge, B. Sirinivasan, Dynamic self organizing maps with controlled growth for knowledge discovery, IEEE Transactions on Neural Networks, Special Issue on Knowledge Discovery and Data Mining, vol. 11, pp. 601614,2000
• [50] N. V. Chawla, Data Mining for Imbalanced Datasets: An Overview, pp. pages 875886, in O. Maimon, L. Rokach (eds), Data Mining and Knowledge Discovery Handbook, Springer, 2010