Generation of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods


AKKOYUN S., Yesilkanat C. M., BAYRAM T.

COMPUTER PHYSICS COMMUNICATIONS, vol.297, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 297
  • Publication Date: 2024
  • Doi Number: 10.1016/j.cpc.2023.109055
  • Journal Name: COMPUTER PHYSICS COMMUNICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Chemical Abstracts Core, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Karadeniz Technical University Affiliated: Yes

Abstract

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 that the obtained results are in harmony with the experimental data and that the methods can be used safely. The obtained results are published on a web page that allows for online calculation of heavy-ion fusion and fusion-evaporation reaction cross-sections.