Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks

Year 2020, Volume: 24 Issue: 5, 1115 - 1120, 01.10.2020

Serkan Akkoyun Hüseyin Kaya

https://doi.org/10.16984/saufenbilder.694382

Cited By: 1

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.

Keywords

Photonuclear reaction, cross-section, calcium, artificial neural network

Supporting Institution

Sivas Cumhuiyet University Scientific Research Projects Coordination Unit

Project Number

F-616

References

• K. Strauch, “Recent Studies of Photonuclear Reactions”, Ann. Rev. Nucl. Sci. vol. 2, pp. 105-128, 1953.
• D. Brajnik, D. Jamnik, G. Kernel, U. Miklavzic and A. Stanovnik, “Photonuclear reactions in 40Ca”, Physical Review C, vol. 9, no. 5, pp. 1901-1918, 1974.
• Y. Utsuno, N. Shimizu, T. Otsuka, S. Ebata and M. Honma, “Photonuclear reactions of calcium isotopes calculated with the nuclear shell model”, Progress in Nuclear Energy, vol. 82, pp. 102-106, 2015.
• A. J. Koning, S. Hilaire, M. Duijvestijn, Proceedings of the International Conference on Nuclear Data for Science and Technology (ND2004), Sep. 26 - Oct.1, 2004, Santa Fe, USA, edited by R.C. Haight, M.B. Chadwick, T. Kawano, P. Talou, AIP Conf. Proc. Vol. 769, pp. 1154, 2005.
• TENDL 2019 Database, https://tendl.web.psi.ch/tendl_2019/gamma_html/Ca/GammaCa.html
• ENDF Nuclear Data File, https://www-nds.iaea.org/exfor/endf.htm
• K. A. Cockell, “CALCIUM | Properties and Determination”, Encyclopedia of Food Sciences and Nutrition (Second Edition), pp. 765-771, 2003.
• L. W. Brady, M. N. Croll, L. Stanhon, D. Hyman and S. Rubins, “Evaluation of Calcium 47 in Normal Man and Its Use in the Evaluation of Bone Healing Following Radiation Therapy in Metastatic Disease”, Radiology, vol. 78, no. 2, pp. 286-288, 1962.
• S. Haykin, “Neural Networks: a Comprehensive Foundation”, Englewood Cliffs, Prentice-Hall, New Jersey, 1999.
• T. Bayram, S. Akkoyun, S. O. Kara, “A study on ground-state energies of nuclei by using neural networks”, Ann. Nucl. Energy vol. 63, pp. 172-175, 2014.
• S. Akkoyun and T. Bayram “Estimations of fission barrier heights for Ra, Ac, Rf and Db nuclei by neural networks“, Int. J. Mod. Phys. E vol. 23, 1450064, 2014.
• S. Akkoyun, T. Bayram, S. O. Kara and A. Sinan, “An artificial neural network application on nuclear charge radii“, J. Phys. G vol. 40, 055106, 2013.
• S. Akkoyun, T. Bayram and T. Turker, “Estimations of beta-decay energies through the nuclidic chart by using neural network”, radiation Physics and Chemistry, vol. 96, pp. 186-189, 2014.
• S. Akkoyun and S. O. Kara, “An approximation to the cross sections of Zl bosonproduction at CLIC by using neural networks”, Cent. Eur. J. Phys. Vol. 11, no. 3, pp. 345-349, 2013.
• S. Akkoyun, S. O. Kara and T. Bayram, “Probing for leptophilic gauge boson Zl ILC with √s=1 TeV by using ANN”, Int.J.Mod.Phys. A, vol. 29, no.30, 1450171, 2014.
• N. Yildiz, S. Akkoyun and H. Kaya, “Consistent Empirical Physical Formula Construction for Gamma Ray Angular Distribution Coefficients by Layered Feedforward Neural Network”, Cumhuriyet Sci. J., vol.39, no. 4, pp. 928-933, 2018.
• S. Akkoyun, T. Bayram and N. Yildiz, “Estimations of Radiation Yields for Electrons in Various Absorbing Materials”, Cumhuriyet Sci. J., vol.37, Special Issue, pp. S59-s65, 2016.
• Matlab, https://www.mathworks.com/discovery/neural-network.html
• K. Levenberg, “A method for the solution of certain non-linear problems in least squares“, Quart. Appl. Math., vol. 2, pp. 164-168, 1944.
• D. Marquardt, D. “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”, SIAM J. Appl. Math., vol. 11, pp. 431-441, 1963.