Applications of different machine learning methods on nuclear charge radius estimations
Yazarlar (3)
Tuncay Bayram
Prof. Dr. Meryem SEFERİNOĞLU Sinop Üniversitesi, Türkiye
Serkan Akkoyun Sinop Üniversitesi
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Physica Scripta (Q2) | ||
| Makale Dili | – | Basım Tarihi | 12-2023 |
| Cilt / Sayı / Sayfa | 98 / 12 / 125310–0 | DOI | 10.1088/1402-4896/ad0434/meta |
| Makale Linki | https://iopscience.iop.org/article/10.1088/1402-4896/ad0434/meta | ||
| UAK Araştırma Alanları |
Dedektör Teknolojisi
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| Özet |
| Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, machine learning (ML) can be considered as a different approach. In this study, ML techniques were performed using the experimental charge radius of 933 atomic nuclei (A ≥ 40 and Z ≥ 20) available in the literature. In the calculations in which eight different approaches were discussed, the obtained outcomes were compared with the experimental data, and the success of each ML approach in estimating the charge radius was revealed. As a result of the study, it was seen that the Cubist model approach was more successful than the others. It has also been observed that ML methods do not miss the different behavior in the magic numbers region. |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 16 |