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Register a new userDetection of fast neutrons and digital pulse-shape discrimination between neutrons and gamma rays
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Added by
P\\\"ar-Anders S\\\"oderstr\\\"om
Publication date
2008
fields
Physics
and research's language is
English
Authors
P.-A. Soderstrom
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The basic principles of detection of fast neutrons with liquid scintillator detectors are reviewed, together with a real example in the form of the Neutron Wall array. Two of the challenges in neutron detection, discrimination of neutrons and gamma rays and identification of cross talk between detectors due to neutron scattering, are briefly discussed, as well as possible solutions to these problems. The possibilities of using digital techniques for pulse-shape discrimination are examined. Results from a digital and anal
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Discrimination of the detection of fast neutrons and gamma rays in a liquid scintillator detector has been investigated using digital pulse-processing techniques. An experimental setup with a 252Cf source, a BC-501 liquid scintillator detector, and a BaF2 detector was used to collect waveforms with a 100 Ms/s, 14 bit sampling ADC. Three identical ADCs were combined to increase the sampling frequency to 300 Ms/s. Four different digital pulse-shape analysis algorithms were developed and compared to each other and to data obtained with an analogue neutron-gamma discrimination unit. Two of the digital algorithms were based on the charge comparison method, while the analogue unit and the other two digital algorithms were based on the zero-crossover method. Two different figure-of-merit parameters, which quantify the neutron-gamma discrimination properties, were evaluated for all four digital algorithms and for the analogue data set. All of the digital algorithms gave similar or better figure-of-merit values than what was obtained with the analogue setup. A detailed study of the discrimination properties as a function of sampling frequency and bit resolution of the ADC was performed. It was shown that a sampling ADC with a bit resolution of 12 bits and a sampling frequency of 100 Ms/s is adequate for achieving an optimal neutron-gamma discrimination for pulses having a dynamic range for deposited neutron energies of 0.3-12 MeV. An investigation of the influence of the sampling frequency on the time resolution was made. A FWHM of 1.7 ns was obtained at 100 Ms/s.
MIMAC (MIcro-TPC MAtrix of Chambers) is a directional WIMP Dark Matter detector project. Direct dark matter experiments need a high level of electron/recoil discrimination to search for nuclear recoils produced by WIMP-nucleus elastic scattering. In this paper, we proposed an original method for electron event rejection based on a multivariate analysis applied to experimental data acquired using monochromatic neutron fields. This analysis shows that a $10^5$ rejection power is reachable for electron/recoil discrimination. Moreover, the efficiency was estimated by a Monte-Carlo simulation showing that a 105 electron rejection power is reached with a $86.49pm 0.17$% nuclear recoil efficiency considering the full energy range and $94.67pm0.19$% considering a 5~keV lower threshold.
The cross-sections of prompt gamma-ray production from $^{nat}$Sn and $^{nat}$C elements induced by 14.1-MeV neutrons were measured. The time-of-flight technique was used for n-gamma discrimination. The experimental results were compared with theoretical calculations performed by Empire 3.2 and Talys 1.6 codes using different models for photon strength function and nuclear level density.
Ultracold neutrons provide a unique tool for the study of neutron properties. An overview is given of the ultracold neutron (UCN) source at PSI, which produces the highest UCN intensities to fundamental physics experiments by exploiting the high intensity proton beam in combination with the high UCN yield in solid deuterium at a temperature of 5K. We briefly list important fundamental physics results based on measurements with neutrons at PSI.
In this work, a new neutron and {gamma}(n/{gamma}) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and {gamma} data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and {gamma} events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/{gamma} discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/{gamma} discrimination. The FOM increases from 0.907 {pm} 0.034 to 0.953 {pm} 0.037 by using the new method of the ENN. The proposed n/{gamma} discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
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