Research Article
Shell Model Calculations for Proton-rich Zn Isotopes via New Generated Effective Interaction by Artificial Neural Networks
Abstract
In this study, the artificial
neural network method has been employed for the generation of the new two-body
matrix elements which is used for fpg shell nuclei. For this purpose, jj44b
interaction Hamiltonian has been considered as a source. After the generation
of the new Hamiltonian, both, original and new generated, are tested on
proton-rich Zn isotopes. According to the results, the calculated values are
close to the each other. As well the results from new interaction (jj44b_nn)
are closer to the available experimental values in some cases.
References
- [1] Mayer, M.G., On Closed Shells in Nuclei. II, Phys.Rev. 75 (1949) 1969.
- [2] Jensen, J.H.D., et al.,On the "Magic Numbers" in Nuclear Structure, Phys.Rev. 75 (1949) 1766.
- [3] Mayer, M.G., On Closed Shells in Nuclei, Phys.Rev. 74 (1948) 235.
- [4] Mayer, M.G., Nuclear Configurations in the Spin-Orbit Coupling Model. I. Empirical Evidence, Phys.Rev. 78 (1950) 16.
- [5] Talmi, I., 55 Years Of The Shell Model: A Challenge To Nuclear Many-Body Theory, Int.J.Mod.Phys.E 14 (2005) 821.
- [6] Caurier, E., etal., The shell model as a unified view of nuclear structure, Rev.Mod.Phy. 77 (2005) 427.
- [7] Brown, B.A., The nuclear shell model towards the drip lines, Prog.Part.Nucl.Phys. 47 (2001) 517.
- [8] Brown, B.A. and Lisetskiy, A.F., unpublished. The jj44b Hamiltonian was obtained from a fit to about 600 binding energies and excitation energies with a method similar to that used for the JUN45 Hamiltonian.
- [9] Neufcourt, L., et al., Bayesian approach to model-based extrapolation of nuclear observables, Phys.Rev. C 98 (2018) 034318.
- [10] Negoita, G.A., et al., Deep Learning: A Tool for Computational Nuclear Physics, arXiv:1803.03215 [physics.comp-ph] (2018).
- [11] Yildiz, N., et al., Consistent Empirical Physical Formula Construction for Gamma Ray Angular Distribution Coefficients by Layered Feedforward Neural Network, Cumhuriyet Sci.J., 39 (2018) 928.
- [12] Bayram, T., et al., A study on ground-state energies of nuclei by using neural networks, Ann.Nucl.Energy., 63 (2014) 172.
- [13] Akkoyun, S., et al., An artificial neural network application on nuclear charge radii, J. Phys. G: Nucl. Part. Phys., 40 (2013) 055106.
- [14] Shimizu, N. Nuclear shell-model code for massive paralel computation, KSHELL, arXiv:1310.5431 [nucl-th] (2013).
- [15] Haykin, S., Neural Networks: A Comprehensive Foundation (Englewood Cliffs, NJ: Prentice-Hall) (1999).
- [16] Brown, B.A., Rae, W.D.M., The Shell-Model Code NuShellX@MSU Nucl.Data Sheets. 120 (2014) 115.
- [17] REDSTICK, http://www.phys.lsu.edu/faculty/cjohnson/redstick.html
- [18] Jhonson, C.W., et al., BIGSTICK: A flexible configuration-interaction shell-model code, arXiv:1801.08432v1 [physics.comp-ph] (2018).
- [19] ANTOINE, http://www.iphc.cnrs.fr/nutheo/code_antoine/menu.html
- [20] B. A. Brown, et al., Oxbash for Windows, MSU_NSCL report number 1289.
Yapay Sinir Ağı ile Yeni Üretilen Etkin Etkileşimle Nötron Zengini Zn İzotopları için Kabuk Modeli Hesaplamaları
Abstract
Bu çalışmada, fpg kabuk çekirdekleri için
kullanılan iki cisim matris elemanlarının üretilmesi için yapay sinir ağı
yöntemi kullanılmıştır. Bu amaçla, jj44b etkileşim Hamiltonian’i, kaynak olarak
kabul edilmiştir. Yeni Hamiltonian'ın oluşumundan sonra, hem orijinal hem de yeni
üretilen etkileşimin her ikisi de nötronca zengin Zn izotopları üzerinde test
edilmiştir. Elde edilen sonuçlara göre hesaplanan değerler birbirine yakındır.
Ayrıca, yeni etkileşimden (jj44b_nn) elde edilen sonuçlar, mevcut deneysel
değerlere ve literatür değerlerine daha yakın sonuç vermiştir.
Keywords
References
- [1] Mayer, M.G., On Closed Shells in Nuclei. II, Phys.Rev. 75 (1949) 1969.
- [2] Jensen, J.H.D., et al.,On the "Magic Numbers" in Nuclear Structure, Phys.Rev. 75 (1949) 1766.
- [3] Mayer, M.G., On Closed Shells in Nuclei, Phys.Rev. 74 (1948) 235.
- [4] Mayer, M.G., Nuclear Configurations in the Spin-Orbit Coupling Model. I. Empirical Evidence, Phys.Rev. 78 (1950) 16.
- [5] Talmi, I., 55 Years Of The Shell Model: A Challenge To Nuclear Many-Body Theory, Int.J.Mod.Phys.E 14 (2005) 821.
- [6] Caurier, E., etal., The shell model as a unified view of nuclear structure, Rev.Mod.Phy. 77 (2005) 427.
- [7] Brown, B.A., The nuclear shell model towards the drip lines, Prog.Part.Nucl.Phys. 47 (2001) 517.
- [8] Brown, B.A. and Lisetskiy, A.F., unpublished. The jj44b Hamiltonian was obtained from a fit to about 600 binding energies and excitation energies with a method similar to that used for the JUN45 Hamiltonian.
- [9] Neufcourt, L., et al., Bayesian approach to model-based extrapolation of nuclear observables, Phys.Rev. C 98 (2018) 034318.
- [10] Negoita, G.A., et al., Deep Learning: A Tool for Computational Nuclear Physics, arXiv:1803.03215 [physics.comp-ph] (2018).
- [11] Yildiz, N., et al., Consistent Empirical Physical Formula Construction for Gamma Ray Angular Distribution Coefficients by Layered Feedforward Neural Network, Cumhuriyet Sci.J., 39 (2018) 928.
- [12] Bayram, T., et al., A study on ground-state energies of nuclei by using neural networks, Ann.Nucl.Energy., 63 (2014) 172.
- [13] Akkoyun, S., et al., An artificial neural network application on nuclear charge radii, J. Phys. G: Nucl. Part. Phys., 40 (2013) 055106.
- [14] Shimizu, N. Nuclear shell-model code for massive paralel computation, KSHELL, arXiv:1310.5431 [nucl-th] (2013).
- [15] Haykin, S., Neural Networks: A Comprehensive Foundation (Englewood Cliffs, NJ: Prentice-Hall) (1999).
- [16] Brown, B.A., Rae, W.D.M., The Shell-Model Code NuShellX@MSU Nucl.Data Sheets. 120 (2014) 115.
- [17] REDSTICK, http://www.phys.lsu.edu/faculty/cjohnson/redstick.html
- [18] Jhonson, C.W., et al., BIGSTICK: A flexible configuration-interaction shell-model code, arXiv:1801.08432v1 [physics.comp-ph] (2018).
- [19] ANTOINE, http://www.iphc.cnrs.fr/nutheo/code_antoine/menu.html
- [20] B. A. Brown, et al., Oxbash for Windows, MSU_NSCL report number 1289.
There are 20 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Natural Sciences |
| Authors | |
| Publication Date | September 30, 2019 |
| Submission Date | March 2, 2019 |
| Acceptance Date | July 29, 2019 |
| Published in Issue | Year 2019 |