Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
More options …

Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks

Tatiana Tambouratzis
  • Department of Industrial Management & Technology, University of Piraeus, addressStreet107 Deligiorgi St., CityPiraeus 185 34, country-regionplaceGreece
/ Dina Chernikova
  • Division of Nuclear Engineering, Chalmers University of Technology SE-412 96 CityplaceGothenburg, country-regionSweden
/ Imre Pzsit
  • Division of Nuclear Engineering, Chalmers University of Technology SE-412 96 CityplaceGothenburg, country-regionSweden
Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0006

Abstract

The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (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 (γ’s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the training set. the SOM exhibits higher n/γ identification rales when classifying new paltms regardless of the proportion of training and test set patterns at the different energy levels: the average tint: for decision making equals 100 /e in the cax of the LVQ and 450 μs in the case of the SOM.

References

  • [1] R. T. Kouzes, The 3He Supply Problem, Report 413 No. PNNL-18388, Pacific 414 Northwest National Laboratory, Richland, WA, 2009Google Scholar

  • [2] A. Enqvist, placeI. Pzsit, S. Avdic, Sample characterization using both neutron and gamma multiplicities, Nuclear Instruments & Methods A, vol. 615, pp. 62-69, 2010Web of ScienceGoogle Scholar

  • [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, 2008Web of ScienceGoogle Scholar

  • [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, 2011Google Scholar

  • [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, 2012Google Scholar

  • [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 2012Google Scholar

  • [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, 2013Google Scholar

  • [8] F. D. Brooks, A scintillation counter with neutron and gamma-ray discriminators, Nuclear Instruments and Methods, vol. 4, pp. 151-163, 1959Google Scholar

  • [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, 1968Google Scholar

  • [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, 1974Google Scholar

  • [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, 2002Google Scholar

  • [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, 2007Web of ScienceGoogle Scholar

  • [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, 2010Web of ScienceGoogle Scholar

  • [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, 2010Web of ScienceGoogle Scholar

  • [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, 1995Google Scholar

  • [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), 2009Google Scholar

  • [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, 2011Google Scholar

  • [18] R. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning representations by back propagating errors, Nature, vol. 323, pp. 533-536, 1986Google Scholar

  • [19] M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the rprop algorithm, Proceedings of the IEEE Conference on Neural Networks, San Francisci, CA, March 28th-April 1st , 1993, pp. 586-591, 1993Google Scholar

  • [20] M. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, vol. 6, pp. 525-533, 1993Google Scholar

  • [21] Z. Cao, L. F. Miller, M. Buckner, Implementation of dynamic bias for classifiers, Nuclear Instruments and Methods A, vol. 416, pp. 438-445, 1998Google Scholar

  • [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, 2004Google Scholar

  • [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 Google Scholar

  • [24] D. Wisniewski, M. Wisniewska, P. Bruyndonckx, M. Krieguer, S. Tavernier, O. Devroede, C.Google Scholar

  • 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, 2005Google Scholar

  • [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, 2006Google Scholar

  • [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, 2008Google Scholar

  • [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, 2009Google Scholar

  • [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, 2009Google Scholar

  • [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, 2012Google Scholar

  • [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, 2013Google Scholar

  • [31] T. Kohonen, Learning vector quantization for pattern recognition, Report TKK-F-A601, Helsinki University of Technology, Espoo, Finland, 1986Google Scholar

  • [32] T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol. 43, no. 1, pp. 59-69, 1982Google Scholar

  • [33] MATLAB R2009a, Mathworks, February 2009Google Scholar

  • [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, 2013Web of ScienceGoogle Scholar

  • [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, 2013Google Scholar

  • [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. 1995Google Scholar

  • [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, 1990Google Scholar

  • [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, 1996Google Scholar

  • [39] T. Kohonen, The self-organizing map.Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990Google Scholar

  • [40] T. Kohonen, Statistical pattern recognition revisited, Advanced Neural Computers, pp. 137-144, 1990Google Scholar

  • [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, 1995Google Scholar

  • [42] M. T. Hagan, H. B. Demuth, M. Beale, Neural Networks Design, PWS Publishing, CO, U.S.A., 1996Google Scholar

  • [43] T. Kohonen, SelfOrganizing Maps. Springer, Berlin, 1995Google Scholar

  • [44] S. Haykin, Selforganizing maps (chapter 9), in Neural Networks A Comprehensive Foundation (2nd ed.), PrenticeHall, 1999Google Scholar

  • [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, 2007Google Scholar

  • [46] S. Kaski, Data Exploration Using SelfOrganizing Maps. PhD thesis, Helsinki University of Technology, Espoo, Finland, 1997Google Scholar

  • [47] A. Hmlinen, Selforganizing map and reduced kernel density estimation, PhD thesis, University of Jyvskyl, Jyvskyl, Finland, 1995Google Scholar

  • [48] H. ShahHosseini, R. Reza, TASOM: a new time adaptive selforganizing map, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 33, pp. 271-282, 2003Google Scholar

  • [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, 2000Google Scholar

  • [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 Google Scholar

About the article

Published Online: 2014-12-30

Published in Print: 2013-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0006.

Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in