NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Issue Date:
2020
Abstract(summary):
Two machine learning techniques, one supervised (Artificial Neural Network) and the other unsupervised (k-means++) have been applied to the task of n/gamma discrimination in Li-7-enriched CLYC detectors, and compared to traditional charge-comparison methods. The results show that a very basic artificial neural network can provide very good discrimination in the energy range investigated, and the k-means++ algorithm is capable of separating neutrons and gamma-rays in CLYC scintillators as well as suggesting reasonable window parameters for charge comparison methods.
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