Applications of different machine learning methods on nuclear charge radius estimations
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Physics
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
Anahtar Kelimeler
Artificial intelligence, Machine learning, Nuclear charge radius, Nuclidic chart
Kaynak
Physica Scripta
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
12
Sayı
1
