Advanced predictive modelling of electric quadrupole transitions in even-even nuclei using various machine learning approaches

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dc.authoridAli, Ahmed H./0000-0002-1525-1406
dc.contributor.authorBerbache, Sihem
dc.contributor.authorAkkoyun, Serkan
dc.contributor.authorAli, Ahmed H.
dc.contributor.authorKartal, Sebahattin
dc.date.accessioned2025-05-04T16:47:07Z
dc.date.available2025-05-04T16:47:07Z
dc.date.issued2025
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractEmpirical predictions of electric quadrupole transition probabilities, B (E2; 0*-> 2*), in even-even nuclei, are among the principles needed to solve the nuclear structure and collective behaviour. In this study, nine different ML algorithms, gradient boosting machine (GBM), random forest (RF), convolutional neural network (CNN), k-nearest neighbour (KNN), CatBoost, extreme gradient boosting (XGBoost), neural network (NN), support vector machine (SVM) and multiple linear regression (MLR), are evaluated as a different data-driven solution for the prediction of B(E2) values. The outcomes show that ensemble models, in particular GBMs, RF, and XGBoost, provide vastly improved predictive capabilities and generalizing influence while creating strong correlations to experimental data with small prediction errors. On the other hand, deep learning models such as CNN and NN is prone to overfitting, while simpler ones such as MLR and KNN fail to capture the non-linear relationships inherent in nuclear data. The findings underscore the promise of ensemble ML tools for nuclear physics in a scalable, accurate approach for predicting transition probabilities.
dc.identifier.doi10.1016/j.nuclphysa.2025.123058
dc.identifier.issn0375-9474
dc.identifier.issn1873-1554
dc.identifier.scopus2-s2.0-85218852890
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.nuclphysa.2025.123058
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35469
dc.identifier.volume1058
dc.identifier.wosWOS:001438232500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofNuclear Physics A
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250504
dc.subjectNuclear structure
dc.subjectB(e2)
dc.subjectMachine learning
dc.subjectEnsemble model
dc.titleAdvanced predictive modelling of electric quadrupole transitions in even-even nuclei using various machine learning approaches
dc.typeArticle

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