Predictions of Conversion Coefficients for E4, M4, E5 and M5 transitions in Ubh element by Using Artificial Neural Network Model
Öz
The primary decay modes for superheavy nuclei are alpha emission and spontaneous fission. Another decay mode is conversion electron emission which competes with the gamma-ray emission. Internal conversion coefficients, which are defined by the ratio of the number of emitted electron to the number of gamma-rays, are important for experimental and theoretical nuclear structure studies. These coefficients can be calculated by many experimental and theoretical ways. Furthermore, there are many computer code for calculations of these coefficients. In this work, the conversion coefficients belonging to the super heavy Unbihexium (Ubh) whose atomic number is 126 were calculated for E4 and E5 (electric), M4 and M5 (magnetic) multipole orders in the transition in low energy range. For the calculations, a mathematical model artificial neural network method was used.
Anahtar Kelimeler
Superheavy Unbihexium internal conversion coefficient electric transition magnetic transition artificial neural network.
Kaynakça
- Rose, M.E., Internal Conversion Coefficients, North-Holland, Amsterdam, 1958.
- Rösel, F., Fries, H.M. ve Alder, K., Internal conversion coefficients for all atomic shells, Atomic Data and Nuclear Data Tables, 21 (1978) 91.
- Pauli, H.C. ve Raff, U., A computer program for internal conversion coefficients and particle parameters, Comput. Phys. Commun., 9 (1975) 392.
- Kibédi, T., vd., Evaluation of theoretical conversion coefficients using BrIcc, Nucl. Instrum. Meth. A., 589 (2008) 202.
- Bayram, T., Akkoyun, S. ve Kara, S.O., A study on ground-state energies of nuclei by using neural networks, Annals of Nuclear Energy, 63 (2014) 172.
- Akkoyun, S., Bayram, T., Kara, S.O. ve Sinan, A., An artificial neural network application on nuclear charge radii, J. Phys. G: Nucl. Part. Phys. 40 (2013) 055106.
- Akkoyun, S., Bayram, T. ve Türker, T., Estimations of beta-decay energies through the nuclidic chart by using neural network, Radiation Physics and Chemistry 96 (2014) 186.
- Costris N. vd., A Global Model of Beta(-) Decay Half-Lives Using Neural Networks. arXiv:nucl-th/0701096v1.
- Mavrommatis E., Gernoth K. A. ve Clark J.W. One and Two Proton Separation Energies from Nuclear Mass Systematics Using Neural Networks. arXiv: nucl-th/0509075.
- Peterson K. L. Classification of Cm II and Pu I Energy Levels Using Counter Propagation Neural Networks, Phys. Rev. A., 44 (1991) 126.
- Bass, S.A., Bischoff, A., Maruhn, JA., St¨ocker, H. ve Greiner, W., Neural networks for impact parameter determination, Phys. Rev. C, 53 (1996 ) 2358.
- Haddad, F., vd., Impact parameter determination in experimental analysis using a neural network, Phys. Rev. C, 55 (1997) 1371.
- David, C., Freslier, M. ve Aichelin, J., Impact parameter determination for heavy-ion collisions by use of a neural network, Phys. Rev. C, 51 (1995) 1453.
- Band, I.M., vd., Dirac–Fock Internal Conversion Coefficients, Atomic Data and Nuclear Data Tables, 81 (2202) 1.
- Kibédi, T., vd., Conversion coefficients for superheavy elements, Atomic Data and Nuclear Data Tables, 98 (2012) 313.
- Haykin, S., Neural Networks: A Comprehensive Foundation, (Englewood Cliffs, NJ: Prentice-Hall), 1999.
- Hamzaçebi, C., Yapay Sinir Ağları, Ekin Basım Yayın Dağıtım, 2011.
- Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, 2012.
- Neurosolutions. http://www.neurosolutions.com/. Levenberg, K., A Method for the Solution of Certain Problems in Least Squares, Quart. Appl. Math. 1944. V. 2. P. 164Ä168.
- Marquardt, D., An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM J. Appl. Math. 1963. V. 11. P. 431Ä441.
Ubh Elementinin E4, M4, E5 ve M5 Geçişleri İçin Dönüşüm Katsayılarının Yapay Sinir Ağları Modeli ile Tahmin Edilmesi
Öz
Süperağır çekirdekler için öncelikli bozunum modları, alfa yayınlanması ve kendiliğinden fisyondur. Diğer bir önemli mod ise, gama yayınlanması ile yarışan, dönüşüm elektron yayınlanmasıdır. Dönüşüm elektronlarının gama ışınlarına oranı olarak bilinen iç dönüşüm katsayıları, teorik ve deneysel nükleer yapı çalışmalarında önemlidir. Bu katsayılar deneysel ve teorik yollarla hesaplanabilmektedir. Ayrıca, bu katsayıların hesaplanması için geliştirilmiş pek çok bilgisayar programı da mevcuttur. Bu çalışmada, Z=126 atom numaralı Unbihexium (Ubh) süperağır çekirdeğine ait dönüşüm katsayıları, düşük geçiş enerji aralığında, E4 ve E5 (elektrik), M4 ve M5 (manyetik) geçişleri için, bir matematiksel model olan yapay sinir ağları metodu kullanılarak elde edilmiştir.
Abstract. The primary decay modes for superheavy nuclei are alpha emission and spontaneous fission. Another decay mode is conversion electron emission which competes with the gamma-ray emission. Internal conversion coefficients, which are defined by the ratio of the number of emitted electron to the number of gamma-rays, are important for experimental and theoretical nuclear structure studies. These coefficients can be calculated by many experimental and theoretical ways. Furthermore, there are many computer code for calculations of these coefficients. In this work, the conversion coefficients belonging to the super heavy Unbihexium (Ubh) whose atomic number is 126 were calculated for E4 and E5 (electric), M4 and M5 (magnetic) multipole orders in the transition in low energy range. For the calculations, a mathematical model artificial neural network method was used.
Anahtar Kelimeler
Süperağır Unbihexium iç dönüşüm katsayısı elektrik geçiş manyetik geçiş yapay sinir ağı
Kaynakça
- Rose, M.E., Internal Conversion Coefficients, North-Holland, Amsterdam, 1958.
- Rösel, F., Fries, H.M. ve Alder, K., Internal conversion coefficients for all atomic shells, Atomic Data and Nuclear Data Tables, 21 (1978) 91.
- Pauli, H.C. ve Raff, U., A computer program for internal conversion coefficients and particle parameters, Comput. Phys. Commun., 9 (1975) 392.
- Kibédi, T., vd., Evaluation of theoretical conversion coefficients using BrIcc, Nucl. Instrum. Meth. A., 589 (2008) 202.
- Bayram, T., Akkoyun, S. ve Kara, S.O., A study on ground-state energies of nuclei by using neural networks, Annals of Nuclear Energy, 63 (2014) 172.
- Akkoyun, S., Bayram, T., Kara, S.O. ve Sinan, A., An artificial neural network application on nuclear charge radii, J. Phys. G: Nucl. Part. Phys. 40 (2013) 055106.
- Akkoyun, S., Bayram, T. ve Türker, T., Estimations of beta-decay energies through the nuclidic chart by using neural network, Radiation Physics and Chemistry 96 (2014) 186.
- Costris N. vd., A Global Model of Beta(-) Decay Half-Lives Using Neural Networks. arXiv:nucl-th/0701096v1.
- Mavrommatis E., Gernoth K. A. ve Clark J.W. One and Two Proton Separation Energies from Nuclear Mass Systematics Using Neural Networks. arXiv: nucl-th/0509075.
- Peterson K. L. Classification of Cm II and Pu I Energy Levels Using Counter Propagation Neural Networks, Phys. Rev. A., 44 (1991) 126.
- Bass, S.A., Bischoff, A., Maruhn, JA., St¨ocker, H. ve Greiner, W., Neural networks for impact parameter determination, Phys. Rev. C, 53 (1996 ) 2358.
- Haddad, F., vd., Impact parameter determination in experimental analysis using a neural network, Phys. Rev. C, 55 (1997) 1371.
- David, C., Freslier, M. ve Aichelin, J., Impact parameter determination for heavy-ion collisions by use of a neural network, Phys. Rev. C, 51 (1995) 1453.
- Band, I.M., vd., Dirac–Fock Internal Conversion Coefficients, Atomic Data and Nuclear Data Tables, 81 (2202) 1.
- Kibédi, T., vd., Conversion coefficients for superheavy elements, Atomic Data and Nuclear Data Tables, 98 (2012) 313.
- Haykin, S., Neural Networks: A Comprehensive Foundation, (Englewood Cliffs, NJ: Prentice-Hall), 1999.
- Hamzaçebi, C., Yapay Sinir Ağları, Ekin Basım Yayın Dağıtım, 2011.
- Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, 2012.
- Neurosolutions. http://www.neurosolutions.com/. Levenberg, K., A Method for the Solution of Certain Problems in Least Squares, Quart. Appl. Math. 1944. V. 2. P. 164Ä168.
- Marquardt, D., An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM J. Appl. Math. 1963. V. 11. P. 431Ä441.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Editöriyal |
| Yazarlar | |
| Yayımlanma Tarihi | 19 Mart 2014 |
| Yayımlandığı Sayı | Yıl 2014 Cilt: 35 Sayı: 1 |