Deep neural network predictions for excitation functions of 165Ho(α, xn) reactions
 
Yazarlar (4)
R. Gokhan Tuereci Kirikkale University, Türkiye
Ismail Hakki Sarpuen Akdeniz University, Türkiye
Prof. Dr. Abdullah AYDIN Kırşehir Ahi Evran Üniversitesi, Türkiye
Sadiye M. Cakmak Eskisehir Osmangazi 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.2025.112075
Özet
Accurate nuclear reaction cross section data are essential for nuclear medicine, reactor technology, and nuclear astrophysics. In this study, excitation functions for Ho(α,n)Tm, Ho(α,2n)Tm, Ho(α,3n)Tm and Ho(α,4n)Tm reactions are analyzed over a wide range of alpha incident energies. Experimental data from EXFOR are compared with theoretical predictions generated using the TALYS nuclear reaction code and the TENDL-2023 evaluated nuclear data library. Additionally, a data-driven approach utilizing Deep Neural Networks (DNNs) with various activation functions (ReLU, ELU, LeakyReLU, SiLU, Mish, PReLU) is developed to predict the cross sections. Python programming language and pytorch module are used in the DNN predictions. The results demonstrate that while conventional models provide a reasonable approximation of reaction trends, Artificial Neural Network (ANN) models which are a branch of machine learning significantly improve agreement with experimental data. These findings underscore the potential of artificial intelligence as a complementary tool for enhancing nuclear reaction modeling. In addition, using different activation functions in the deep learning algorithm is important to get the best results in the predictions of the experimental data.
Anahtar Kelimeler
Deep neural network | Pythorch, effect of activation functions | Excitation functions | Ho-165(alpha,xn) reactions | Talys code
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Deep neural network predictions for excitation functions of 165Ho(α, xn) reactions

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