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Register a new userDiscrimination of neutrons and {gamma}-rays in liquid scintillator based on Elman neural network
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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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This paper presents measurements of the scintillation light yield and time profile for a number of concentration of water-based liquid scintillator, formulated from linear alkylbenzene (LAB) and 2,5-diphenyloxazole (PPO). We find that the scintillation light yield is linear with the concentration of liquid scintillator in water between 1 and 10% with a slope of 127.9+-17.0 ph/MeV/concentration and an intercept value of 108.3+-51.0 ph/MeV, the latter being illustrative of non-linearities with concentration at values less than 1%. This is larger than expected from a simple extrapolation of the pure liquid scintillator light yield. The measured time profiles are consistently faster than that of pure liquid scintillator, with rise times less than 250ps and prompt decay constants in the range of 2.1-2.85ns. Additionally, the separation between Cherenkov and scintillation light is quantified using cosmic muons in the CHESS experiment for each formulation, demonstrating an improvement in separation at the centimeter scale. Finally, we briefly discuss the prospects for large-scale detectors.
This paper presents studies of the performance of water-based liquid scintillator in both 1-kt and 50-kt detectors. Performance is evaluated in comparison to both pure water Cherenkov detectors and a nominal model for pure scintillator detectors. Performance metrics include energy, vertex, and angular resolution, along with a metric for ability to separate the Cherenkov from the scintillation signal, as being representative of various particle identification capabilities that depend on the Cherenkov / scintillation ratio. We also modify the time profile of scintillation light to study the same performance metrics as a function of rise and decay time. We go on to interpret these results in terms of their impact on certain physics goals, such as solar neutrinos and the search for Majorana neutrinos. This work supports and validates previous results, and the assumptions made therein, by using a more complete detector model and full reconstruction. We confirm that a high-coverage, 50-kt detector would be capable of better than 10 (1)% precision on the CNO neutrino flux with a WbLS (pure LS) target in 5 years of data taking. A 1-kt LS detector, with a conservative 50% fiducial volume of 500~t, can achieve a better than 5% detection. Using the liquid scintillator model, we find a sensitivity into the normal hierarchy region for Majorana neutrinos, with half life sensitivity of $T^{0 ubetabeta}_{1/2} > 1.4 times 10^{28}$ years at 90% CL for 10 years of data taking with a Te-loaded target.
A successfull application of Geiger-mode multipixel avalanche diodes (GMAPDs) for pulse-shape discrimination in alpha-beta spectrometry using organic liquid scintillator is described in this paper. Efficient discrimination of alpha and beta components in the emission of radioactive isotopes is achieved for alpha energies above 0.3 MeV. The ultra-compact design of the scintillating detector helps to efficiently suppress cosmic-ray and ambient radiation background. This approach allows construction of hand-held robust devices for monitoring of radioactive contamination in various environmental conditions.
Liquid-scintillator-based detectors are a robust technology that scales well to large volumes. For this reason, they are attractive for experiments searching for neutrinoless double-beta decay. A combination of improved photo-detection technology and novel liquid scintillators may allow for the extraction of particle direction in addition to the total energy of the particle. Such an advance would find applications beyond searches for neutrinoless double-beta decay.
Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product $^{10}$C is an important background in the region of interest between 2-3 MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network that uses the temporal and spatial correlations in light emissions to identify $^{10}$C background events. With a typical kiloton-scale detector configuration like the KamLAND detector, we find that the algorithm is capable of identifying 61.6% of the $^{10}$C at 90% signal acceptance. A detector with perfect light collection could identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.
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