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116,117,118,119,120,124Sn ve233,234,235,236,238U İzotopları İçin Dev Dipol Rezonans Enerjilerinin Kestirimi

Year 2017, Volume: 17 Issue: 2, 426 - 431, 31.08.2017

Serkan Akkoyun , Tuncay Bayram Yücel Özgüven

Abstract

Dev dipol rezonans (GDR) parametrelerini elde etmek için birçok deneysel ve teorik metot uygulanmaktadır.Bu çalışmada, Sn ve U izotopları için GDR enerjileri, yapay sinir ağları (YSA) metodu ile tahmin edilmiştir. Sonuçlara göre, YSA’nın eğitiminde deneysel verilerden ortalama sapma, %1 seviyesindedir. Sn ve U izotopları için tahmin edilen enerjilerdeki ortalama kare hata, 0,034 MeV’dir.Teorik bir model için ise hata, 0,061 MeV’dir.Bu sonuç, GDR enerjileri üzerinde ANN tahmininin, teorik hesaplamalardaki sonuçlardan daha iyi olduğunu göstermektedir.

Keywords

Dev dipol rezonans E1 geçişi yapay sinir ağları

References

  • Akkoyun, S. and Bayram, T.2014. Estimations of fission barrier heights for Ra, Ac, Rf and Db nuclei by neural networks. International Journal of Modern Physics E 23, 1450064
  • Akkoyun, S., Bayram, T. and Kar,a S.O.2015. A study on estimation of electric quadrupole transition probability in nuclei. Journal of Nuclear Sciences 2, 7-10.
  • Akkoyun, S., Bayram, T., Kara, S.O. and Sinan, A.2013. An artificial neural network application on nuclear charge radii. J. Phys. G Nucl. Partic., 40, 055106.
  • Akkoyun, S., Bayram, T., Kara, S.O. and Yıldız, N. 2013. Consistent empirical physical formulas for potential energy curves of 38-66Ti isotopes by using neural networks. Physics of Particles and Nuclei Letters 10, 528-534.
  • Baldwin, G.C. and Klaiber, G.S. 1948. X-ray Yield Curves for gamma-neutron Reactions. Phys. Rev. 73, 1156.
  • Bayram, T., Akkoyun, S. and Kara, S.O. 2014. A study on ground-state energies of nuclei by using neural networks. Annals of Nuclear Energy, 63, 172-175.
  • Bayram, T., Akkoyun, S. and Kara, S.O.2014. α-decay half-life calculations of superheavy nuclei using artificial neural networks. Journal of Physics: Conference Series 490, 012105.
  • Berman, B.L. and Fultz, S.C.1975. Measurements of the giant dipole resonance with monoenergetic photons. Rev. Mod. Phys., 47, 713.
  • Bothe, W. and Gentner, W. 1937. Atomumwandlungen durch gamma-Strahlen. Z. Phys. 106, 236-248.
  • Chomaz, Ph. 1997. Collective excitations in nuclei. Ganil Laboratory Commun IN2P3 (CNRS). Costiris, N., Mavrommatis, E., Gernoth, K.A. and Clark, J.W. 2007. A Global Model of 𝛽− Decay Half- Lives Using Neural Networks. arXiv:nucl-th/0701096. Dietrich, S.S. and Berman, B.L. 1988. Atlas of Photoneutron Cross Sections Obtained with Monoenergetic Photons. Atomic Data and Nuclear Data Tables 38, 199-338.
  • Goriely, S. 1998. Radiative neutron captures by neutron-rich nuclei and the r-process nucleosynthesis. Phys. Lett. B436, 10-18.
  • Günoğlu, K., Mavi, B. and Akkurt, İ. 2011. Estimatıon Of Global Radiatıon With Artıfıcıal Neural Networks (Ann) Method. e-Journal of New World Sciences Academy,6-2, 1A0174. Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. Prentice-Hall Inc., Englewood Cliffs, NJ, USA. Jianfeng, L. and Zongdi, S. 1995. Chinese J. Nucl. Phys., 17, 336.
  • Kawatsu, C. and Shevin, M. 2003. Parameters forthe Hot Giant Dipole Resonance. Preprint submitted to Atomic Data and Nuclear Data Tables. Levenberg, K.1944. A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quart. Appl. Math., Vol. 2, 164-168
  • . Marquardt, D. 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math., Vol. 11, 431-441.
  • Plujko, V.A., Gorbachenko, O.M., Bondar, V.M. and Capote, R.2011. Renewed Database of GDR Parameters for Atomic Nuclei. Journal of the Korean Physical Society, 59-2, 1514-1517.
  • Schiller, A. and Thoennessen, M. 2007. Compilation of giant electric dipole resonances built on excited states. Atomic Data and Nuclear Data Tables 93, 549–573.
  • Spicer, B.M. 1969. The Giant Dipole Resonance. Advances in Nuclear Physics, 2, 1-78. Yeşilkanat, C.M., Kobya, Y., Taşkın, H. and Çevik, U. 2014. Yapay Sinir ağları yöntemi ile Artvin ilinde ölçülen gama doz oranlarının ara değer modellemesi ve haritalanması. Cumhuriyet Science Journal 35, 36-52.
  • 1-https://www-nds.iaea.org/RIPL-2/gamma/gdr-parameters-exp.dat., (01.01.2016)
  • 2-http://www-nds.iaea.org/exfor/., (01.01.2016)
  • 3-http://www.neurosolutions.com/., (01.01.2016)

Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes

Year 2017, Volume: 17 Issue: 2, 426 - 431, 31.08.2017

Serkan Akkoyun , Tuncay Bayram Yücel Özgüven

Abstract

Several experimental and thoretical methods are applied for obtaining giant dipole resonance (GDR) parameters. In this study, GDR energies for Sn and U isotopes have been predicted by using artificial neural network (ANN) method. According to the results, in the training of the ANN, the mean deviations from the experimental values are in the order of 1%. The mean square error for the estimated energies of Sn and U isotopes is 0.034 MeV. Similar error value belonging to a theoretical model calculation is 0.061 MeV. This result indicates that ANN predictions on GDR energy give better results according to the theoretical results.

Keywords

Giant dipole resonance E1 transition artificial neural networks

References

  • Akkoyun, S. and Bayram, T.2014. Estimations of fission barrier heights for Ra, Ac, Rf and Db nuclei by neural networks. International Journal of Modern Physics E 23, 1450064
  • Akkoyun, S., Bayram, T. and Kar,a S.O.2015. A study on estimation of electric quadrupole transition probability in nuclei. Journal of Nuclear Sciences 2, 7-10.
  • Akkoyun, S., Bayram, T., Kara, S.O. and Sinan, A.2013. An artificial neural network application on nuclear charge radii. J. Phys. G Nucl. Partic., 40, 055106.
  • Akkoyun, S., Bayram, T., Kara, S.O. and Yıldız, N. 2013. Consistent empirical physical formulas for potential energy curves of 38-66Ti isotopes by using neural networks. Physics of Particles and Nuclei Letters 10, 528-534.
  • Baldwin, G.C. and Klaiber, G.S. 1948. X-ray Yield Curves for gamma-neutron Reactions. Phys. Rev. 73, 1156.
  • Bayram, T., Akkoyun, S. and Kara, S.O. 2014. A study on ground-state energies of nuclei by using neural networks. Annals of Nuclear Energy, 63, 172-175.
  • Bayram, T., Akkoyun, S. and Kara, S.O.2014. α-decay half-life calculations of superheavy nuclei using artificial neural networks. Journal of Physics: Conference Series 490, 012105.
  • Berman, B.L. and Fultz, S.C.1975. Measurements of the giant dipole resonance with monoenergetic photons. Rev. Mod. Phys., 47, 713.
  • Bothe, W. and Gentner, W. 1937. Atomumwandlungen durch gamma-Strahlen. Z. Phys. 106, 236-248.
  • Chomaz, Ph. 1997. Collective excitations in nuclei. Ganil Laboratory Commun IN2P3 (CNRS). Costiris, N., Mavrommatis, E., Gernoth, K.A. and Clark, J.W. 2007. A Global Model of 𝛽− Decay Half- Lives Using Neural Networks. arXiv:nucl-th/0701096. Dietrich, S.S. and Berman, B.L. 1988. Atlas of Photoneutron Cross Sections Obtained with Monoenergetic Photons. Atomic Data and Nuclear Data Tables 38, 199-338.
  • Goriely, S. 1998. Radiative neutron captures by neutron-rich nuclei and the r-process nucleosynthesis. Phys. Lett. B436, 10-18.
  • Günoğlu, K., Mavi, B. and Akkurt, İ. 2011. Estimatıon Of Global Radiatıon With Artıfıcıal Neural Networks (Ann) Method. e-Journal of New World Sciences Academy,6-2, 1A0174. Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. Prentice-Hall Inc., Englewood Cliffs, NJ, USA. Jianfeng, L. and Zongdi, S. 1995. Chinese J. Nucl. Phys., 17, 336.
  • Kawatsu, C. and Shevin, M. 2003. Parameters forthe Hot Giant Dipole Resonance. Preprint submitted to Atomic Data and Nuclear Data Tables. Levenberg, K.1944. A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quart. Appl. Math., Vol. 2, 164-168
  • . Marquardt, D. 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math., Vol. 11, 431-441.
  • Plujko, V.A., Gorbachenko, O.M., Bondar, V.M. and Capote, R.2011. Renewed Database of GDR Parameters for Atomic Nuclei. Journal of the Korean Physical Society, 59-2, 1514-1517.
  • Schiller, A. and Thoennessen, M. 2007. Compilation of giant electric dipole resonances built on excited states. Atomic Data and Nuclear Data Tables 93, 549–573.
  • Spicer, B.M. 1969. The Giant Dipole Resonance. Advances in Nuclear Physics, 2, 1-78. Yeşilkanat, C.M., Kobya, Y., Taşkın, H. and Çevik, U. 2014. Yapay Sinir ağları yöntemi ile Artvin ilinde ölçülen gama doz oranlarının ara değer modellemesi ve haritalanması. Cumhuriyet Science Journal 35, 36-52.
  • 1-https://www-nds.iaea.org/RIPL-2/gamma/gdr-parameters-exp.dat., (01.01.2016)
  • 2-http://www-nds.iaea.org/exfor/., (01.01.2016)
  • 3-http://www.neurosolutions.com/., (01.01.2016)
There are 20 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Serkan Akkoyun

Tuncay Bayram This is me

Yücel Özgüven This is me

Publication Date August 31, 2017
Submission Date October 24, 2016
Published in Issue Year 2017 Volume: 17 Issue: 2

Cite

APA Akkoyun, S., Bayram, T., & Özgüven, Y. (2017). Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(2), 426-431.
AMA Akkoyun S, Bayram T, Özgüven Y. Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. August 2017;17(2):426-431.
Chicago Akkoyun, Serkan, Tuncay Bayram, and Yücel Özgüven. “Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17, no. 2 (August 2017): 426-31.
EndNote Akkoyun S, Bayram T, Özgüven Y (August 1, 2017) Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17 2 426–431.
IEEE S. Akkoyun, T. Bayram, and Y. Özgüven, “Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 17, no. 2, pp. 426–431, 2017.
ISNAD Akkoyun, Serkan et al. “Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17/2 (August 2017), 426-431.
JAMA Akkoyun S, Bayram T, Özgüven Y. Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2017;17:426–431.
MLA Akkoyun, Serkan et al. “Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 17, no. 2, 2017, pp. 426-31.
Vancouver Akkoyun S, Bayram T, Özgüven Y. Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2017;17(2):426-31.


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