Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2020, Cilt: 24 Sayı: 5, 1115 - 1120, 01.10.2020
https://doi.org/10.16984/saufenbilder.694382

Öz

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.

Destekleyen Kurum

Sivas Cumhuiyet University Scientific Research Projects Coordination Unit

Proje Numarası

F-616

Kaynakça

  • 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.
Yıl 2020, Cilt: 24 Sayı: 5, 1115 - 1120, 01.10.2020
https://doi.org/10.16984/saufenbilder.694382

Öz

Proje Numarası

F-616

Kaynakça

  • 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.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Metroloji,Uygulamalı ve Endüstriyel Fizik
Bölüm Araştırma Makalesi
Yazarlar

Serkan Akkoyun 0000-0002-8996-3385

Hüseyin Kaya Bu kişi benim

Proje Numarası F-616
Yayımlanma Tarihi 1 Ekim 2020
Gönderilme Tarihi 25 Şubat 2020
Kabul Tarihi 24 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 24 Sayı: 5

Kaynak Göster

APA Akkoyun, S., & Kaya, H. (2020). Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks. Sakarya University Journal of Science, 24(5), 1115-1120. https://doi.org/10.16984/saufenbilder.694382
AMA Akkoyun S, Kaya H. Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks. SAUJS. Ekim 2020;24(5):1115-1120. doi:10.16984/saufenbilder.694382
Chicago Akkoyun, Serkan, ve Hüseyin Kaya. “Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks”. Sakarya University Journal of Science 24, sy. 5 (Ekim 2020): 1115-20. https://doi.org/10.16984/saufenbilder.694382.
EndNote Akkoyun S, Kaya H (01 Ekim 2020) Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks. Sakarya University Journal of Science 24 5 1115–1120.
IEEE S. Akkoyun ve H. Kaya, “Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks”, SAUJS, c. 24, sy. 5, ss. 1115–1120, 2020, doi: 10.16984/saufenbilder.694382.
ISNAD Akkoyun, Serkan - Kaya, Hüseyin. “Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks”. Sakarya University Journal of Science 24/5 (Ekim 2020), 1115-1120. https://doi.org/10.16984/saufenbilder.694382.
JAMA Akkoyun S, Kaya H. Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks. SAUJS. 2020;24:1115–1120.
MLA Akkoyun, Serkan ve Hüseyin Kaya. “Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks”. Sakarya University Journal of Science, c. 24, sy. 5, 2020, ss. 1115-20, doi:10.16984/saufenbilder.694382.
Vancouver Akkoyun S, Kaya H. Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks. SAUJS. 2020;24(5):1115-20.

30930 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.