Machine Learning Based Classification of the Halos in Light Nuclei Region

Serkan Akkoyun

doi:10.17776/csj.1416907

EN

Machine Learning Based Classification of the Halos in Light Nuclei Region

Abstract

Experimental and theoretical studies on halo nuclei, whose nucleon binding energies are extremely weak, are among the most interesting topics of nuclear physics studies. By better defining and understanding this unusual behavior of these nuclei, our understanding of nuclear structure can be further improved. Although there are already a few experimentally proven halo nuclei in the literature, many others have found their place in the literature as candidate halo nuclei. In this study, the classification of halo nuclei was carried out using an artificial neural network approach. In the light nuclei region, the properties of nuclei, including halo nuclei, were discussed and the existing halo nuclei were classified. The success of the obtained results indicates that machine learning methods can be used for identifying halo nuclei. Thus, these methods are considered as one of the alternative tools to confirm the existence of new or candidate halo nuclei.

Keywords

References

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Details

Primary Language

English

Subjects

Nuclear Physics

Journal Section

Research Article

Authors

Serkan Akkoyun ^*
0000-0002-8996-3385
Türkiye

Publication Date

March 28, 2024

Submission Date

January 9, 2024

Acceptance Date

February 27, 2024

Published in Issue

Year 2024 Volume: 45 Number: 1

DOI

https://doi.org/10.17776/csj.1416907

IZ

https://izlik.org/JA58PM32MU

Cite

RIS / Bibtex

APA

Akkoyun, S. (2024). Machine Learning Based Classification of the Halos in Light Nuclei Region. Cumhuriyet Science Journal, 45(1), 160-163. https://doi.org/10.17776/csj.1416907

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SMOTE-based data augmentation for accurate classification of neutron halo nuclei: A machine learning approach in nuclear physics

Knowledge-Based Systems

https://doi.org/10.1016/j.knosys.2025.113580

Machine Learning Based Classification of the Halos in Light Nuclei Region

Abstract

Keywords

Öz

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

SMOTE-based data augmentation for accurate classification of neutron halo nuclei: A machine learning approach in nuclear physics