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

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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.

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  • [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: the rprop algorithm Proceedings of the IEEE Conference on 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

  • [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 on Systems Man and Cybernetics Part 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

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