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

2013 ◽  
Vol 3 (2) ◽  
pp. 77-88 ◽  
Author(s):  
Tatiana Tambouratzis ◽  
Dina Chernikova ◽  
Imre Pzsit
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gamma Rays ◽  
Pulse Shape ◽  
Energy Levels ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Discrimination Ability ◽  
Artificial Neural ◽  
Self Organising Map

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.

Digital liquid-scintillation counting and effective pulse-shape discrimination with artificial neural networks

2015 ◽  
Vol 103 (1) ◽  
pp. 15-25
Author(s):  
Gert Langrock ◽  
Norbert Wiehl ◽  
Hans-Otto Kling ◽  
Matthias Mendel ◽  
Andrea Nähler ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pulse Shape ◽  
Liquid Scintillation ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Background Count ◽  
High Background ◽  
Pile Up ◽  
Artificial Neural

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.

Solar Flare Energetic Neutral Emission Measurements in the Project Coronas-I

1994 ◽  
Vol 144 ◽  
pp. 635-639
Author(s):  
J. Baláž ◽  
A. V. Dmitriev ◽  
M. A. Kovalevskaya ◽  
K. Kudela ◽  
S. N. Kuznetsov ◽  
...  
Keyword(s):  
Solar Flare ◽  
Energy Range ◽  
Gamma Rays ◽  
Pulse Shape ◽  
Gamma Ray ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Solar Neutron ◽  
Low Altitude

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.

A pulse shape discrimination method with csi using the ratio of areas for identifying neutrons and gamma rays

2013 ◽  
Vol 62 (5) ◽  
pp. 839-844 ◽  
Author(s):  
Jong-Kwan Woo ◽  
Je Wou Ko ◽  
Silin Na ◽  
Yong Joo Kim ◽  
HyoSang Lee
Keyword(s):  
Gamma Rays ◽  
Pulse Shape ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Discrimination Method

scholarly journals Application of artificial neural network in neutron/gamma pulse shape discrimination for EJ301 scintillation detector

2021 ◽  
Vol 4 (2) ◽  
pp. first
Author(s):  
Chuân Văn Phan ◽  
Hải Xuân Nguyễn ◽  
Anh Ngọc Nguyễn ◽  
Hải Xuân Phạm ◽  
Phong Xuân Mai ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Region ◽  
Pulse Shape ◽  
Gamma Ray ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Neuron Network ◽  
Neutron Measurement ◽  
Artificial Neural

The scintilator detectors are sensitive to both neutron and gamma radiation. Therefore, right identification of the pulses which generated by neutrons or gamma ray from these detectors plays an important role in neutron measurement by using scintilator detector. In order to improve the ability to pulse shape discrimination (PSD), many PSD techniques have been studied, developed and applied. In this work, we use a basic configuration of a Fully connected Neural network (Fc- Net) where the number of elements of the network is minimum, and each element corresponds to identified specification of neutron or gamma pulses measured by using EJ-301 scintilator detector. The minimum of error principle has been applied for neuron network design; therefore, the accuracy of recognitions did not affect by this reduced network. The obtained results show that the identify accuracy of FcNet is higher than those of digital charge integration (DCI) method. Being tested using 60Co radioactive source, it is shown that, with the application of the FcNet, the accuracy of the gamma pulses discrimination acquires 98.60% in the energy region from 50 to 2000 keV electron equivalent energy (keVee), and 95.59% in the energy region from 50 to 150 keVee. In general, the obtained results indicate that the artificial neural network method can be applied to build neutron/gamma spectrometers with limited hardware.

Pulse Shape Discrimination with Organic Liquid Scintillator Solutions

1970 ◽  
Vol 24 (4) ◽  
pp. 397-404 ◽  
Author(s):  
Donald L. Horrocks
Keyword(s):  
Gamma Rays ◽  
Pulse Shape ◽  
Slow Component ◽  
Liquid Scintillator ◽  
Organic Liquid ◽  
Delayed Fluorescence ◽  
Alpha Particles ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Decay Times

The source of the delayed fluorescence by triplet—triplet interactions is discussed as the basis of the technique of differentiating between particles of different specific ionizations. The variation in the relative intensity of delayed fluorescence (called the slow component of scintillation) is correlated with the type of ionizing radiation. The decay times of the prompt and slow components do not depend upon the type of ionizing particle. The slow component in liquid scintillator solutions free of dissolved gases (especially oxygen) has a decay time of about 250 × 10−9 sec. Liquid scintillator solutions with pulse shape discrimination properties have been used to measure neutrons (proton recoils) in the presence of gamma rays (Compton scattered electrons). They have also been demonstrated as able to measure the relative activities of alpha particles and fission fragments from a fission source in the presence of gamma and beta background.

scholarly journals Digital pulse shape discrimination between fast neutrons and gamma rays with para-terphenyl scintillator

2017 ◽  
Vol 934 ◽  
pp. 012057 ◽  
Author(s):  
A S Chepurnov ◽  
M A Kirsanov ◽  
A A Klenin ◽  
S G Klimanov ◽  
A S Kubankin
Keyword(s):  
Gamma Rays ◽  
Pulse Shape ◽  
Pulse Shape Discrimination ◽  
Fast Neutrons ◽  
Shape Discrimination ◽  
Digital Pulse
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