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Machine learning n/gamma discrimination in CLYC scintillators

Author:
Doucet, E.  Brown, T.  Chowdhury, P.  Lister, C. J.  Morse, C.  Bender, P. C.  Rogers, A. M.  


Journal:
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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