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

Yıl 2017, Cilt 17, Sayı 2, 426 - 431, 31.08.2017

Öz

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.

Kaynakça

  • 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

Yıl 2017, Cilt 17, Sayı 2, 426 - 431, 31.08.2017

Öz

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.

Kaynakça

  • 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)

Ayrıntılar

Birincil Dil İngilizce
Konular Fen
Bölüm Makaleler
Yazarlar

Serkan AKKOYUN
0000-0002-8996-3385


Tuncay BAYRAM
0000-0002-8996-3385


Yücel ÖZGÜVEN
0000-0002-8996-3385

Yayımlanma Tarihi 31 Ağustos 2017
Başvuru Tarihi 24 Ekim 2016
Kabul Tarihi 18 Temmuz 2017
Yayınlandığı Sayı Yıl 2017, Cilt 17, Sayı 2

Kaynak Göster

Bibtex @araştırma makalesi { akufemubid524662, journal = {Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi}, issn = {}, eissn = {2149-3367}, address = {}, publisher = {Afyon Kocatepe Üniversitesi}, year = {2017}, volume = {17}, pages = {426 - 431}, doi = {}, title = {Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes}, key = {cite}, author = {Akkoyun, Serkan and Bayram, Tuncay and Özgüven, Yücel} }
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 . Retrieved from https://dergipark.org.tr/tr/pub/akufemubid/issue/43399/524662
MLA 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 17 (2017 ): 426-431 <https://dergipark.org.tr/tr/pub/akufemubid/issue/43399/524662>
Chicago 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 17 (2017 ): 426-431
RIS TY - JOUR T1 - Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes AU - Serkan Akkoyun , Tuncay Bayram , Yücel Özgüven Y1 - 2017 PY - 2017 N1 - DO - T2 - Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi JF - Journal JO - JOR SP - 426 EP - 431 VL - 17 IS - 2 SN - -2149-3367 M3 - UR - Y2 - 2017 ER -
EndNote %0 Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes %A Serkan Akkoyun , Tuncay Bayram , Yücel Özgüven %T Giant Dipole Resonence Energy Predictions For 116,117,118,119,120,124Sn and 233,234,235,236,238U Isotopes %D 2017 %J Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi %P -2149-3367 %V 17 %N 2 %R %U
ISNAD Akkoyun, Serkan , Bayram, Tuncay , Özgüven, Yücel . "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 (Ağustos 2017): 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. 2017; 17(2): 426-431.
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): 426-431.
IEEE S. Akkoyun , T. Bayram ve 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, c. 17, sayı. 2, ss. 426-431, Ağu. 2017