Estimation of fission barrier heights for even–even superheavy nuclei using machine learning approaches
| dc.authorid | 0000-0002-7508-7548 | en_US |
| dc.contributor.author | Yeşilkanat, Cafer Mert | |
| dc.contributor.author | Akkoyun, Serkan | |
| dc.date.accessioned | 2023-04-14T12:54:08Z | |
| dc.date.available | 2023-04-14T12:54:08Z | |
| dc.date.issued | 2023 | |
| dc.department | AÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümü | en_US |
| dc.description.abstract | With the fission barrier height information, the survival probabilities of super-heavy nuclei can also be reached. Therefore, it is important to have accurate knowledge of fission barriers, for example, the discovery of super-heavy nuclei in the stability island in the super-heavy nuclei region. In this study, five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network were used to accurately predict the fission barriers of 330 even–even super-heavy nuclei in the region 140 ? N ? 216 with proton numbers between 92 and 120. The obtained results were compared both among themselves and with other theoretical model calculation estimates and experimental results. According to the results obtained, it was concluded that the Cubist model, support vector regression and extreme gradient boosting methods generally gave better results and could be a better tool for estimating fission barrier heights. | |
| dc.identifier.citation | Yeşilkanat, C. M., & Akkoyun, S. (2023). Estimation of fission barrier heights for even–even superheavy nuclei using machine learning approaches. Journal of Physics G: Nuclear and Particle Physics, 50(5), | en_US |
| dc.identifier.doi | 10.1088/1361-6471/acbaaf | |
| dc.identifier.issue | 5 | en_US |
| dc.identifier.uri | https://hdl.handle.net/11494/4870 | |
| dc.identifier.volume | 50 | en_US |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.institutionauthor | Yeşilkanat, Cafer Mert | |
| dc.language.iso | en | en_US |
| dc.publisher | Iop Science | en_US |
| dc.relation.ispartof | Journal of Physics G: Nuclear and Particle Physics | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.title | Estimation of fission barrier heights for even–even superheavy nuclei using machine learning approaches | en_US |
| dc.type | Article |
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