Generation of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods
Yazarlar (3)
Serkan Akkoyun
Prof. Dr. Meryem SEFERİNOĞLU Sinop Üniversitesi, Türkiye
Prof. Dr. Tuncay Bayram Sinop Üniversitesi
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Computer Physics Communications (Q1) | ||
| Makale Dili | – | Basım Tarihi | 04-2024 |
| Cilt / Sayı / Sayfa | 297 / 0 / 109055–0 | DOI | – |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S0010465523004009 | ||
| UAK Araştırma Alanları |
Dedektör Teknolojisi
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| Özet |
| In order to obtain cross-sections of heavy-ion fusion and fusion-evaporation reactions, artificial neural networks, cubist, random forest, support vector regression, extreme gradient boosting, and multiple linear regression machine learning approaches were used separately in this study. The outcomes from these different methods that are obtained from the training carried out with the existing experimental data in the literature were compared. Furthermore, it has been observed that a two-step process yielded better results for determining the heavy-ion reaction cross-sections, after first estimating which approach would be better for which reaction. In this manner, the method for which the cross-section needs to be calculated is determined by the machine learning classification application, and predictions can be made using the machine learning regression application with the determined method. It has been concluded … |
| Anahtar Kelimeler |
