Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach

Dell’Aquila, Daniele; Russo, M. (2020) Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach. Computer Physics Communications, 259 . ISSN 00104655

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Abstract

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus–nucleus collisions at low and intermediate energies.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence in nuclear data ; classification of data in nucleus-nucleus collisions ; genetic programming ; artificial neural networks
Subjects:	NATURAL SCIENCES > Physics
Divisions:	Division of Experimental Physics
Depositing User:	Ema Buhin Šaler
Date Deposited:	04 May 2026 07:55
URI:	https://fulir.irb.hr:/id/eprint/11854
DOI:	10.1016/j.cpc.2020.107667

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