Applications of different machine learning methods on nuclear charge radius estimations

Basit Öğe Kaydı
dc.authorid0000-0003-3704-0818
dc.authorid0000-0002-7508-7548
dc.authorid0000-0002-8996-3385
dc.contributor.authorBayram, Tuncay
dc.contributor.authorYeşilkanat, Cafer Mert
dc.contributor.authorAkkoyun, Serkan
dc.date.accessioned2025-07-11T12:25:16Z
dc.date.available2025-07-11T12:25:16Z
dc.date.issued2023
dc.departmentAÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümü
dc.description.abstractTheoretical 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.
dc.identifier.doi10.1088/1402-4896/ad0434
dc.identifier.issn00318949
dc.identifier.issue1
dc.identifier.scopuss2.0-85177984000
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://hdl.handle.net/11494/5775
dc.identifier.volume12
dc.indekslendigikaynakScopus
dc.institutionauthorYeşilkanat, Cafer Mert
dc.institutionauthorid0000-0002-7508-7548
dc.language.isoen
dc.publisherInstitute of Physics
dc.relation.ispartofPhysica Scripta
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectNuclear charge radius
dc.subjectNuclidic chart
dc.titleApplications of different machine learning methods on nuclear charge radius estimations
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
5775.pdf
Boyut:
1.28 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Matematik ve Fen Bilimleri Eğitimi Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu