Deep learning estimations of the production cross sections of 77Br medical radionuclide
Yazarlar (4)
Prof. Dr. Abdullah AYDIN
Kırşehir Ahi Evran Üniversitesi, Türkiye
R. Gokhan Tureci
Kirikkale University, Türkiye
Ismail Hakki Sarpun
Akdeniz University, Türkiye
Hasan Ozdogan
Antalya Bilim University, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | APPLIED RADIATION AND ISOTOPES (Q2) | ||
| Dergi ISSN | 0969-8043 Wos Dergi Scopus Dergi | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2025 |
| Cilt / Sayı / Sayfa | 225 / 0 / – | DOI | 10.1016/j.apradiso.2025.112003 |
| 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 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |