ﻻ يوجد ملخص باللغة العربية
Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.
Using the waveforms from a digital electronic system, an offline analysis technique on pulse shape discrimination (PSD) has been developed to improve the neutron-gamma separation in a bar-shaped NE-213 scintillator that couples to a photomultiplier t
The fast neutron rate is measured at the site of NEOS experiment, a short baseline neutrino experiment located in a tendon gallery of a commercial nuclear power plant, using a 0.78-liter liquid scintillator detector. A pulse shape discrimination tech
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variation
We report on the characterization of two inverted coaxial Ge detectors in the context of being employed in future $^{76}$Ge neutrinoless double beta ($0 ubetabeta$) decay experiments. It is an advantage that such detectors can be produced with bigger
The GERDA experiment located at the LNGS searches for neutrinoless double beta (0 ubetabeta) decay of ^{76}Ge using germanium diodes as source and detector. In Phase I of the experiment eight semi-coaxial and five BEGe type detectors have been deploy