研究目的
Investigation of two aspects of neutron-γ discrimination: a comparison of the pulse-shape properties of regular and deuterated liquid scintillators BC-501A and BC-537, and how the application of Artificial Neural Networks (ANNs) can be used to improve the discrimination properties.
研究成果
The results show that using artificial neural networks, it is possible to achieve more than 95% γ-ray rejection efficiency over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, the lower light output of BC-537 results in a higher cut-off energy for separating neutrons and γ rays compared to BC-501A. This is a significant disadvantage for BC-537 in applications where low-energy interactions dominate.
研究不足
The study acknowledges potential systematic uncertainties in the energy calibration due to assumptions about the Compton edge position in BC-537 and the non-linear behavior of neutron and γ-ray energy deposition in the scintillators. The translation of measured light into neutron energy is not straightforward due to these non-linearities.
1：Experimental Design and Method Selection:
The study focused on comparing the performance of BC-501A and BC-537 liquid scintillators in neutron and γ-ray discrimination using artificial neural networks and charge comparison methods.
2：Sample Selection and Data Sources:
Data were collected using several γ-ray sources and a 252Cf neutron source. The detectors were cylindrical, filled with either BC-501A or BC-537, and coupled to Philips XP4512B photomultiplier tubes.
3：List of Experimental Equipment and Materials:
The setup included liquid scintillator detectors, photomultiplier tubes, a BaF2 detector for time-of-flight measurements, and digitizers from Struck Innovative Systems for signal processing.
4：Experimental Procedures and Operational Workflow:
Signals from the detectors were split into digital and analogue data acquisition systems. Neutrons and γ-rays were identified using three-dimensional cuts on total charge, time-of-flight, and analogue pulse-shape discrimination parameters.
5：Data Analysis Methods:
The performance of the discrimination algorithms was evaluated using one-dimensional time-of-flight distributions as an observable of the type of incoming radiation.
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