Pregled bibliografske jedinice broj: 1088468
Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach
Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach // Computer physics communications, 259 (2021), 107667-107667 doi:10.1016/j.cpc.2020.107667 (međunarodna recenzija, članak, ostalo)
CROSBI ID: 1088468 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automatic classification of nuclear physics data
via a Constrained Evolutionary Clustering approach
Autori
Dell’Aquila, Daniele ; Russo, Marco
Izvornik
Computer physics communications (0010-4655) 259
(2021);
107667-107667
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo
Ključne riječi
Artificial intelligence in nuclear data ; classification of data in nucleus-nucleus collisions ; genetic programming ; artificial neural networks
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Fizika
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
