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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

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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


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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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. (CC BY-NC-ND 3.0)

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