Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach
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- @Article{Dell'Aquila:2021:cpc,
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author = "D. Dell'Aquila and M. Russo",
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title = "Automatic classification of nuclear physics data via a
Constrained Evolutionary Clustering approach",
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journal = "Computer Physics Communications",
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year = "2021",
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volume = "259",
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pages = "107667",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Nuclear
physics data classification, Evolutionary computing,
Clustering algorithms, Charged particle identification
in nuclear collisions",
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ISSN = "0010-4655",
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URL = "https://www.sciencedirect.com/science/article/pii/S0010465520303234",
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DOI = "doi:10.1016/j.cpc.2020.107667",
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size = "41 pages",
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abstract = "This paper presents an automatic method for data
classification in nuclear physics experiments based on
evolutionary computing and vector quantisation. 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.",
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notes = "Also known as \cite{DELLAQUILA2021107667}",
- }
Genetic Programming entries for
Daniele Dell'Aquila
Marco Russo
Citations