A study on estimation of electric quadrupole transition probability in nuclei
Yıl 2015,
, 7 - 10, 05.01.2015
S. Akkoyun
,
T. Bayram
,
S.o. Kara
Öz
The reduced electric quadrupole transition probability (B(E2)↑) between 0+ ground state and 2+ state in nuclei is an important quantity because it represents basic nuclear information on energies of low-lying levels in the nuclei. It provides knowledge about deformation of nuclei. In this study, the B(E2)↑ values of some even-even nuclei in 110 A 190 region have been estimated by using artificial neural network (ANN) method which is a non-linear approximator. The present study shows that ANN is found to be useful in order to predict B(E2)↑ values of even-even nuclei in this region.
Kaynakça
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- P.O. Lipas, J. Savolainen, Nuclear Physics A 130, 77-87 (1969).
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- J.D. Walecka, Ann. Phys. (N.Y.) 83, 491 (1974).
- A.K. Dutta, J.-P. Arcoragi, J.M. Pearson, R. Behrman, F. Tondeur, Nucl. Phys. A 458, 77-94 (1986).
- Bonche, P. Flocard, H. Heenen, P.H. Krieger, S.J. Weiss, M.S. Nucl. Phys. A 1985, 443, 39-63 (1986).
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- S. Akkoyun, T. Bayram, S.O. Kara, Cumhuriyet Science Journal 34-1, 42-51 (2013).
- S. Dragoviç, A. Onjia, Applied Radiation and Isotopes 63, 363-366 (2005).
- S. Akkoyun, T. Bayram, S.O. Kara, A. Sinan, J. Phys. G Nucl. Partic. 40, 055106 (2013).
- S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice-Hall Inc., Englewood Cliffs, NJ, USA, 1999.
- K. Levenberg, Quart. Appl. Math. Vol. 2, 164-168 (1944).
- D. Marquardt, SIAM J. Appl. Math. Vol. 11, 431-441 (1963).
Yıl 2015,
, 7 - 10, 05.01.2015
S. Akkoyun
,
T. Bayram
,
S.o. Kara
Kaynakça
- S. Raman, C.W. Nestor, JR.P. Tikkanen, Atomic data and Nuclear Data Tables, 78, 1-128 (2001).
- P.O. Lipas, J. Savolainen, Nuclear Physics A 130, 77-87 (1969).
- P. Möller, J.R. Nix, Nucl. Phys. A 536, 20-60 (1992).
- S. Cwiok, J. Dudek, W. Nazarewicz, J. Skalski, T. Werner, Comput. Phys. Commun. 46, 379-399 (1987).
- J.D. Walecka, Ann. Phys. (N.Y.) 83, 491 (1974).
- A.K. Dutta, J.-P. Arcoragi, J.M. Pearson, R. Behrman, F. Tondeur, Nucl. Phys. A 458, 77-94 (1986).
- Bonche, P. Flocard, H. Heenen, P.H. Krieger, S.J. Weiss, M.S. Nucl. Phys. A 1985, 443, 39-63 (1986).
- B. Nerlo-Pomorska, K. Pomorski, M. Brack, E. Werner, Nucl. Phys. A 462, 252 (1987).
- T. Bayram, S. Akkoyun S.O. Kara, Annals of Nuclear Energy 63, 172-175 (2014).
- C. David, M. Freslier, J. Aichelin, Phys. Rev. C 51, 3, 1453-1459 (1995).
- S.A. Bass, A. Bischoff, J.A. Maruhn, H. Stöcker, W. Greiner, Phys. Rev. C 53, 5, 2358-2363 (1996).
- F. Haddad, K. Hagel, J. Li, N. Mdeiwayeh, J.B. Natowitz, R. Wada, B. Xiao, C. David, M. Freslier, J. Aichelin, Phys. Rev. C 55, 3,1371-1375 (1997).
- N. Costiris, E. Mavrommatis, K.A. Gernoth, J.W. Clark, arXiv:nucl-th/0701096.
- S. Akkoyun, T. Bayram, S.O. Kara, Cumhuriyet Science Journal 34-1, 42-51 (2013).
- S. Dragoviç, A. Onjia, Applied Radiation and Isotopes 63, 363-366 (2005).
- S. Akkoyun, T. Bayram, S.O. Kara, A. Sinan, J. Phys. G Nucl. Partic. 40, 055106 (2013).
- S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice-Hall Inc., Englewood Cliffs, NJ, USA, 1999.
- K. Levenberg, Quart. Appl. Math. Vol. 2, 164-168 (1944).
- D. Marquardt, SIAM J. Appl. Math. Vol. 11, 431-441 (1963).