Yazar "Kaya, Huseyin" seçeneğine göre listele

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    Determination of Photonuclear Reaction Cross-Sections on Stable P-shell Nuclei by Using Deep Neural Networks
    (Springer, 2023) Akkoyun, Serkan; Kaya, Huseyin; Seker, Abdulkadir; Yesilyurt, Saliha
    Photonuclear reactions are widely used in investigations of nuclear structure. Thus, the determination of the cross-sections are essential for the experimental studies. In the present work, (gamma, n) photonuclear reaction cross-sections for stable p-shell nuclei have been estimated by using the neural network method. The main purpose of this study is to find neural network structures that give the best estimations for the cross-sections, and to compare them with the available data. These comparisons indicate the deep neural network structures that are convenient for this task. Through this procedure, we have found that the shallow NN models, tanh activation function is better than the ReLU. However, as our models become deeper, the difference between tanh and ReLU decreases considerably. In this context, we think that the crucial hyperparameters are the size of the hidden layer and neuron numbers of each layer.
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    Neural Network Estimation for Attenuation Coefficients for Gamma-Ray Angular Distribution
    (Pleiades Publishing Inc, 2019) Akkoyun, Serkan; Yildiz, Nihat; Kaya, Huseyin
    Spins of nuclear states (J) and multipolarities of gamma rays are usually investigated by the angular distribution of gamma rays emitted from aligned states formed by nuclear reactions. In the case of partial alignment, attenuation coefficients are used in angular distribution function. These coefficients are tabulated in literature for different J values. However, these coefficients involve r-fold tensor products. Furthermore, as the calculation of these coefficients implicitly involves highly complicated integral quantities, they are very difficult to handle explicitly for larger values. In this respect, universal nonlinear function approximator layered feedforward neural network (LFNN) can be applied to construct consistent empirical physical formulas (EPFs) for physical phenomena. In this paper, we consistently estimated the attenuation coefficients by constructing suitable LFNNs. The LFNN-EPFs fitted the literature coefficient data very well. Moreover, magnificent LFNN test set predictionson unseen data confirmed the consistent LFNN-EPFs for the determination of coefficients.