scholarly journals Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks

2020 ◽  
Vol 1 (4) ◽  
pp. 045022
Author(s):  
J Griffiths ◽  
S Kleinegesse ◽  
D Saunders ◽  
R Taylor ◽  
A Vacheret
Author(s):  
Tatiana Tambouratzis ◽  
Dina Chernikova ◽  
Imre Pzsit

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.


2015 ◽  
Vol 103 (1) ◽  
pp. 15-25
Author(s):  
Gert Langrock ◽  
Norbert Wiehl ◽  
Hans-Otto Kling ◽  
Matthias Mendel ◽  
Andrea Nähler ◽  
...  

Abstract A typical problem in low-level liquid scintillation (LS) counting is the identification of α particles in the presence of a high background of β and γ particles. Especially the occurrence of β-β and β-γ pile-ups may prevent the unambiguous identification of an α signal by commonly used analog electronics. In this case, pulse-shape discrimination (PSD) and pile-up rejection (PUR) units show an insufficient performance. This problem was also observed in own earlier experiments on the chemical behaviour of transactinide elements using the liquid-liquid extraction system SISAK in combination with LS counting. α-particle signals from the decay of the transactinides could not be unambiguously assigned. However, the availability of instruments for the digital recording of LS pulses changes the situation and provides possibilities for new approaches in the treatment of LS pulse shapes. In a SISAK experiment performed at PSI, Villigen, a fast transient recorder, a PC card with oscilloscope characteristics and a sampling rate of 1 giga samples s−1 (1 ns per point), was used for the first time to record LS signals. It turned out, that the recorded signals were predominantly α, β-β and β-γ pile up, and fission events. This paper describes the subsequent development and use of artificial neural networks (ANN) based on the method of “back-propagation of errors” to automatically distinguish between different pulse shapes. Such networks can “learn” pulse shapes and classify hitherto unknown pulses correctly after a learning period. The results show that ANN in combination with fast digital recording of pulse shapes can be a powerful tool in LS spectrometry even at high background count rates.


1994 ◽  
Vol 144 ◽  
pp. 635-639
Author(s):  
J. Baláž ◽  
A. V. Dmitriev ◽  
M. A. Kovalevskaya ◽  
K. Kudela ◽  
S. N. Kuznetsov ◽  
...  

AbstractThe experiment SONG (SOlar Neutron and Gamma rays) for the low altitude satellite CORONAS-I is described. The instrument is capable to provide gamma-ray line and continuum detection in the energy range 0.1 – 100 MeV as well as detection of neutrons with energies above 30 MeV. As a by-product, the electrons in the range 11 – 108 MeV will be measured too. The pulse shape discrimination technique (PSD) is used.


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