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A clustering algorithm for scintillator signals applied to neutron and gamma patterns identification

TitleA clustering algorithm for scintillator signals applied to neutron and gamma patterns identification
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2019
AuthorsPollastrone, F., Cardarilli G.C., Riva M., R. Pereira Costa, Fernandes A., Cruz N., Podda S., Pompili F., Pillon M., Angelone Massimo, Marocco D., and Belli F.
JournalFusion Engineering and Design
ISSN09203796
KeywordsClustering algorithms, Digital signal processing, Filter techniques, Frascati neutron generators, Gamma rays, General description, High-energy particles, Matched filters, Neutron sources, Neutrons, Nuclear application, Pattern Recognition, Pattern recognition algorithms, Pulse amplifiers, Reference patterns, Scintillation counters, Scintillator signals
Abstract

In several nuclear applications, scintillators, coupled with a photomultiplier and pulse amplifier, are used in order to detect high energy particles, i.e. neutrons and gamma rays. The different particles incident on the scintillator produce electrical pulses having different shape; moreover, the amplitude of these signals is related to the particles energy. The electrical pulses of the scintillator chain are acquired by digital systems that, generally, perform a triggered acquisition consisting of a stream of pulse windows. The aim of this study is the development of a simplified clustering algorithm able to produce reference patterns in compliance with the pattern recognition algorithm based on the matched filter technique, starting from a stream of pulses generated by particles having different energy and type. This paper contains a general description of the clustering algorithm and of the main customizations performed for the scintillator signals. In order to test in real case the efficiency, the algorithm has been applied on the data acquired during a radiation test performed at Frascati Neutron Generator for Stilbene scintillator. The results show that this algorithm works properly, deriving the centroids of the clusters representing the neutron and gamma shapes, together with their occurrences in the analysed data stream. © 2019

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85063113696&doi=10.1016%2fj.fusengdes.2019.03.117&partnerID=40&md5=301e13efaba5313517ea7afc1c6f6f05
DOI10.1016/j.fusengdes.2019.03.117
Citation KeyPollastrone2019