Serkan AKKOYUN, Tuncay BAYRAM

Production Cross-Section of 51Cr Radioisotope Using Artificial Neural Networks

Purpose: The use of radioisotopes in diagnosis and treatment in medicine is increasing day by day. In order to make the production of these radioisotopes efficiently, the production cross-sections must be calculated correctly. In the absence of experimental data, cross-sections are calculated in various theoretical ways and the data corresponding to the desired energy value is obtained. In our study, using artificial neural networks as a different approach, an alternative model is presented to estimate cross-sections at unknown neutron energies. Material and Methods: Artificial neural networks method was used to obtain cross-sections of 51Cr radioisotopes produced by neutron-induced reactions. By taking this cross-section data available in the literature, 80% of it was used in the training of the network and the remaining 20% was used in the test. The inputs of artificial neural networks are the incident neutron energies and the output is the cross-section. Hidden layer neuron number 20 was used that gave the best results after many trials. Results: According to the results we have obtained, the artificial neural network method can be used as an alternative method to estimate the radioisotope production cross-sections. While the MSE value of the estimations made over the training data is 0.178 barn, the MSE value on the test data is 0.155 barn. Correlation coefficient values of the predictions of the network on training and test data were found as 0.93 and 0.95, respectively. Conclusion: When compared with the experimental results in the literature, it is concluded that the results of artificial neural networks can be used as an alternative to estimate the cross-section. An advantage of this method is that it allows to obtain results quickly without going into complex mathematical formulation. The results obtained from this study are an indication that cross-sections of any reaction to be performed using any isotope can be obtained by using artificial neural networks method.

Keywords:

Radioisotope;, cross-section; Cr isotope;, artificial neural network;,

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Akkoyun, S., (2020). Estimation of fusion reaction cross-sections by artificial neural networks. Nuclear Instruments and Methods in Physics Research Section B, 462, 51-54. https://doi.org/10.1016/j.nimb.2019.11.014
Akkoyun, S. and Kaya, H. (2020). Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks, Sakarya University Journal of Science 24, 1117-1122. https://doi.org/10.16984/saufenbilder.694382
Akkoyun, S., Kaya, H., Şeker, A. and Yeşilyurt, S. (2020). Determination of Photonuclear Reaction Cross-Sections on stable p-shell Nuclei by Using Deep Neural Networks. arXiv:2003.07050
Bailey, D. L., Humm, J. L., Todd-Pokropek, A., and van Aswegen, A. (2014). Nuclear medicine physics. International Atomic Energy Agency, Division of Human Health, Vienna (Austria).
Gleason, G. (1975). Thermal Neutron Cross Sections and (n,γ) Resonance Integrals Part I. Radiochemical and Radioanalytical Letters, 23, 317-323.
Haykin, S. (1999). Neural Networks: a Comprehensive Foundation. Englewood Cliffs, Prentice-Hall, New Jersey.
Kapchigashev, S. P. & Popov, Y. P. (1964). Capture cross sections for neutrons of energy up to 50 kev for Cr, Cr50, Cr52, Cr53 Nuclei. Atomnaya Energiya, 16, 306-308.
Kenny M. J., Allen B. J., de L.Musgrove, A. R., Macklin, R. L., & Halperin, J. (1977). Neutron capture by the chromium isotopes. (AAEC/E-400). Lucas Heights, N.S.W.: Australian Atomic Energy Commission.
Kopecky, J. Atlas of Neutron Capture Cross Section. (1997). IAEA NUCLEAR DATA SECTION, Wagramerstrasse 5, A-1400 Vienna.
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2, 164-168.
Marquardt, D. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM Journal of Applied Mathematics, 11, 431-441.
Martin, J.E. (2013). Physics for radiation protection. Wiley-VCH.
Pomerance, H. (1952). Thermal Neutron Capture Cross Sections. Physical Review, 88, 412-413. https://doi.org/10.1103/PhysRev.88.412
Simonits, A., De Corte, F., Moens, L., & Hoste, J. (1984). Critical Evaluation and Experimental Determination of the Nuclear Activation and Decay Parameters for the Reactions: 50Cr(n,γ)51Cr,58Fe(n,γ)59Fe,109Ag(n,γ)110mAg. Journal of Radioanalytical and Nuclear Chemistry, 81, 369-396.
Sims, G. H. E & Juhnke, D. G. (1968). The Thermal Neutron Cross-Sections and Resonance Integrals of 50Cr, 109Ag, 123Sb, 133Cs, 191Ir and 202Hg. Journal of Inorganic and Nuclear Chemistry, 30, 349-353. https://doi.org/10.1016/0022-1902(68)80459-2
Stieglitz, R. G., Hockenbury, R. W., & Block, R. C. (1971). Neutron Capture and Transmission Measurements on 50Cr, 52Cr, 53Cr, 54Cr, 60Ni and V, Nuclear Physics A, 163, 592-624. https://doi.org/10.1016/0375-9474(71)90512-4
Venturini, L. & Pecequilo, B. R. S. (1997). Thermal neutron capture cross-section of 48-Ti, 51-V, 50-,52-,53-Cr and 58-,60-,62-,64-Ni. Applied Radiation and Isotopes, 48, 493-496. https://doi.org/10.1016/S0969-8043(96)00285-0