| dc.creator |
KARAMAN, ONUR |
|
| dc.creator |
Ali Üncü, Yiğit |
|
| dc.creator |
Özdoğan, Hasan |
|
| dc.creator |
ŞEKERCİ, Mert |
|
| dc.creator |
KAPLAN, Abdullah |
|
| dc.date |
2021-03-01T00:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:46:50Z |
|
| dc.date.available |
2021-12-03T11:46:50Z |
|
| dc.identifier |
aae38344-ebe1-49cb-84c8-32640be80f67 |
|
| dc.identifier |
10.1016/j.apradiso.2020.109581 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/aae38344-ebe1-49cb-84c8-32640be80f67/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/94071 |
|
| dc.description |
© 2020In this study; Giant Dipole Resonance (GDR) parameters of the spherical nucleus have been estimated by using artificial neural network (ANN) algorithms. The ANN training has been carried out with the Levenberg–Marquardt feed-forward algorithm in order to provide fast convergence and stability in ANN training and experimental data, taken from Reference Input Parameter Library (RIPL). R values of the system have been found as 0.99636, 0.94649, and 0.98318 for resonance energy, full width half maximum, and resonance cross-section, respectively. Obtained results have been compared with the GDR parameters which are taken from the literature. To validate our findings, newly acquired GDR parameters were then replaced with the existing GDR parameters in the TALYS 1.95 code and 142-146Nd(γ,n)141-145Nd reaction cross-sections have been calculated and compared with the experimental data taken from the literature. As a result of the study, it has been shown that ANN algorithms can be used to calculate the GDR parameters in the absence of the experimental data. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Estimations of giant dipole resonance parameters using artificial neural network |
|
| dc.type |
info:eu-repo/semantics/article |
|