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

Tatiana Tambouratzis (Institutionen för teknisk fysik, Nukleär teknik) ; Dina Chernikova (Institutionen för teknisk fysik, Nukleär teknik) ; Imre Pázsit (Institutionen för teknisk fysik, Nukleär teknik)
Journal of Artificial Intelligence and Soft Computing Research (2083-2567). Vol. 3 (2014), 1, p. 77-88.
[Artikel, refereegranskad vetenskaplig]

The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisa- tion(LVQ)andtheselforganisingmap(SOM)-isexploredforpulseshapediscrimination (PSD), i.e. for distinguishing between neutrons (n’s) and gamma rays (γ’s). The effect that (a) the energy level, and (b) the relative size of the training and test sets, have on iden- tification 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/γ 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.



Denna post skapades 2015-01-19. Senast ändrad 2016-05-23.
CPL Pubid: 211050

 

Institutioner (Chalmers)

Institutionen för teknisk fysik, Nukleär teknik (2006-2015)

Ämnesområden

Beräkningsmatematik

Chalmers infrastruktur