No abstract

No abstract

The nuclear reaction induced by photon is one of the important tools in the investigation of atomic nuclei. In the reaction, a target material is bombarded by photons with high-energies in the range of gamma-ray energy range. In the bombarding process, the photons can statistically be absorbed by a nucleus in the target material. Then the excited nucleus can decay by emitting proton, neutron, alpha and light particles or photons. By performing photonuclear reaction on the target, it can be easily investigated low-lying excited states of the nuclei. In the present work, (γ, n) photonuclear reaction cross-sections on different calcium isotopes have been estimated by using artificial neural network method. The method is a mathematical model that mimics the brain functionality of the creatures. The correlation coefficient values of the method for both training and test phases being 0.99 indicate that the method is very suitable for this purpose.

Multipolarities of gamma rays and spins-parities of nuclear states are usually investigated by the angular distribution of gamma rays emitted from aligned states formed by nuclear reactions. For different multipolarities of the transitions, the distribution shows different characteristics. The distribution is obtained by using angular distribution formula which has literature tabulated coefficients for different spins and multipolarities. However, these coefficients involve -fold tensor products and they are highly nonlinear in nature. 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, as we theoretically proved in a previous paper, universal nonlinear function approximator layered feedforward neural network (LFNN) can be applied to construct consistent empirical physical formulas (EPFs) for nonlinear physical phenomena. In this paper, by concentrating on the integer spins of nuclear states and dipole and quadrupole type multipolarities of the transitions, we consistently estimated the coefficients by constructing suitable LFNNs. The LFNN-EPFs fitted the literature coefficient data very well. Moreover, magnificent LFNN test set forecastings over previously unseen data confirmed the consistent LFNN-EPFs for the determination of coefficients. In this sense, we can conclude that the LFNN consistently infers nonlinear physical laws governing the angular distribution of gamma rays, which are otherwise difficult to obtain by conventional coefficient calculation methods.Özet. Gama ışınlarının multipolariteleri ve nükleer durumların spinleri, genelliklle, nükleer reaksiyonlarla oluşturulan hizalanmış durumlardan yayılan gama ışınlarının açısal dağılımı ile incelenir. Geçişlerin farklı multipolarite değerleri için, dağılım farklı özellikler göstermektedir. Dağıtım, farklı spinler ve çok kutupluluklar için literatürdeki tablolanmış katsayılarve açısal dağılım formülü kullanılarak elde edilir. Bununla birlikte, bu katsayılar r katlı tensör çarpımları içerir ve yapıları oldukça doğrusal olmayan şekildedir. Dahası, bu katsayıların hesaplanması karmaşık integraller içerdiğinden, daha büyük r değerleri için açıkça ele alınması çok zordur. Bu bağlamda, daha önceki bir çalışmamızla teorik olarak ispatlandığımız gibi, doğrusal olmayan fiziksel fenomenler için, tutarlı, ampirik fiziksel formüller (EPF'ler) oluşturmak için, evrensel doğrusal olmayan bir katmanlı beslemeli sinir ağı (LFNN) kullanılabilir. Bu makalede, nükleer durumların tamsayı spinlerine ve geçişlerin dipol ve kuadrupol multipolaritelerine odaklanarak, uygun LFNN'leri inşa ederek katsayıları tutarlı bir şekilde tahmin ettik. LFNN-EPF'ler, literatür katsayısı verisini çok iyi bir şekilde fitledi. Ayrıca, daha önce görülmemiş veriler üzerinde yapılan LFNN test seti tahminleri, katsayıların belirlenmesi için tutarlı LFNN-EPF'leri doğrulamıştır. Bu bağlamda, LFNN'nin, gama ışınlarının açısal

The first excited 2 ? energy states of nuclei give much substantial information related to the nuclear structure. All excited states of nuclei are shown regularities in spin, parity, and energy, including these levels. In the even-even nuclei, the first excited state is generally 2 ? , and the energy values of them increase as the closed shells are approached. The nuclei's excited levels can be investigated using theoretical nuclear models, such as the nuclear shell model. In the present study, we have used artificial neural networks to determine the energies of the first 2 ? states in the even-even nuclei in the nuclidic chart as a function of Z and N numbers for the first time. According to the results, the method is convenient for this goal. One can confidently use the method for predicting the first 2 ? state energy values whose experimental values do not exist in the literature.