| Yazarlar (4) |
Prof. Dr. Abdullah AYDIN
Kırşehir Ahi Evran Üniversitesi, Türkiye |
R. Gokhan Tureci
|
|
Ismail Hakki Sarpun
|
|
Hasan Ozdogan
|
| Özet |
| Bromine-77 has a half-life of 56 h and decays nearly exclusively (99.3 %) by electron capture, with prominent gamma rays at 239.0 and 520.7 keV. Once considered primarily for SPECT imaging, this nuclide is increasingly being evaluated for its potential in Auger electron therapy. In this study, deep learning algorithms with Python programming language are improved to predict the production cross sections of bromine-77 radionuclide. Experimental cross sections data used in artificial neural network were taken from the EXFOR nuclear reactions database. The deep learning results obtained for the Se(p,n)Br, Se(p,2n)Br, Se(p,4n)Br and As(α,2n)Br reactions were compared with the calculation results obtained from the TALYS code. It was observed that the results obtained with deep learning obey the experimental values much better. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | APPLIED RADIATION AND ISOTOPES |
| Dergi ISSN | 0969-8043 Wos Dergi Scopus Dergi |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 11-2025 |
| Cilt No | 225 |
| Doi Numarası | 10.1016/j.apradiso.2025.112003 |
| Atıf Sayıları | |
| WoS | 1 |
