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Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması

Year 2018, Volume: 8 Issue: 2, 602 - 605, 01.06.2018

Hüseyin Aytekin

Abstract

Bu çalışmada, sırasıyla, 20, 28, 50 ve 82 sihirli sayıda protona sahip 36-58Ca, 50-78Ni, 100-138Sn ve 182-220Pb çift-çift çekirdeklerinin iki-nötron ayırma enerjilerini hesaplanmak için bir Yapay Sinir Ağları YSA modeli geliştirildi. Elde edilen sonuçlar, Sıvı Damlası Modeli SDM ve Rölativisttik Ortalama Alan Teori ROAT sonuçları ile karşılaştırıldı

Keywords

Sıvı damlası modeli Yapay sinir ağı Rölativisttik ortalama alan teorisi

References

  • Anghel, S., Danil, CC., Zamfir, VN. 2009. Structure features revealed from the two neutron separation energies. Rom. Journ. Phys.,: 301–319.
  • Athanassopoulos, S., Mavrommatis, E., Gernoth, KA., Clark, JW. 2004. Nuclear mass systematics using neural network. Nucl. Phys. A, 743:222-235.
  • Aytekin, H. 2012. An investigation of the nuclear properties of 24O, 30Si, 30S, 54Ca, and 96Zr new magic nuclei. J Korean Phys. Soc., 61 (12):1965-1969.
  • Aytekin, H., Artun, O. 2013. An investigation of the nuclear structures of even-even nuclei Sr, Zr and Mo isotopes. Mod. Phys. Letts. A, 28: 1350007.
  • Bayram, T., Akkoyun, S., Kara, O. 2014. A study on ground- state energies of nuclei by using neural networks. Ann Nucl Energy, 63: 172–175.
  • Bennaceur, K., Dobaczewski, J. 2005. Comput. Coordinate-space solution of the Skyrme–Hartree–Fock–Bogolyubov equations within spherical symmetry. The program HFBRAD (v1.00). Phys. Commun. 168:96-122.
  • Debes, K., Koenig, A., Gross, HM. 2005. Transfer Functions in Artificial Neural Networks. Supplementary Material for urn:nbn:de:0009-3-1515
  • Diamantaras, KI., Kung, SY. 1996. Principal component neural networks: Theory and applications. New York: J. Wiley.
  • Hornik, K., Stinchcombe, M., White, H. 1989. Multilayer freedforward networks are universal approximators. Neural Netw., 89: 359-366.
  • Horný, M. 2014. Bayesian Networks. Technical Report No.5
  • Lalazissis, GA., Raman, S. Ring, P. 1999. Ground-state properties of even-even nuclei in the relativistic mean-field theory. Atom Data Nucl Data, 71: 1–40.
  • Liquid-Drop Model and the Semiempirical Mass Formula. 2018. http://bcs.whfreeman.com/webpub/Ektron/Tipler%20 Modern%20Physics%206e/More%20Sections/More_Chap- ter_11_1-Liquid-Drop_Model_and_the_Semiempirical_ Mass_Formula.pdf.
  • Ring, P. 1996. Relativistic Mean Field Theory in Finite Nuclei. Prog. Part. Nucl. Phys. 37:193-263.
  • Wang, M., Audi, G., Wapstra, AH., Kondev, FG., MacCormick, M., Xu, X., Pfeiffer, B. 2012. The AME2012 atomic mass evaluation. Chin. Phys. C, 36 (2012): 1603–2014.
  • nuclei 36-58Ca, 50-78Ni, 102-138Sn and 182-220Pb by a developed

Calculation of the two-neutron separation energies of even-even 36-58Ca, 50-78Ni, 102-138Sn and 182-220Pb nuclei by an artificial neural network model

Year 2018, Volume: 8 Issue: 2, 602 - 605, 01.06.2018

Hüseyin Aytekin

Abstract

In this study, an Artificial Neural Network ANN model was developed in order to calculate the two-neutron separation energies S2n for the even-even nuclei 36-58Ca, 50-78Ni, 100-138Sn and 182-220Pb with the magic proton numbers, 20, 28, 50 and 82, respectively. The obtained results were compared with the Liquid Drop Model LDM , Relativistic Mean Field Theory RMFT and the experimental results.

Keywords

Neural network liquid drop model relativistic mean field theory

References

  • Anghel, S., Danil, CC., Zamfir, VN. 2009. Structure features revealed from the two neutron separation energies. Rom. Journ. Phys.,: 301–319.
  • Athanassopoulos, S., Mavrommatis, E., Gernoth, KA., Clark, JW. 2004. Nuclear mass systematics using neural network. Nucl. Phys. A, 743:222-235.
  • Aytekin, H. 2012. An investigation of the nuclear properties of 24O, 30Si, 30S, 54Ca, and 96Zr new magic nuclei. J Korean Phys. Soc., 61 (12):1965-1969.
  • Aytekin, H., Artun, O. 2013. An investigation of the nuclear structures of even-even nuclei Sr, Zr and Mo isotopes. Mod. Phys. Letts. A, 28: 1350007.
  • Bayram, T., Akkoyun, S., Kara, O. 2014. A study on ground- state energies of nuclei by using neural networks. Ann Nucl Energy, 63: 172–175.
  • Bennaceur, K., Dobaczewski, J. 2005. Comput. Coordinate-space solution of the Skyrme–Hartree–Fock–Bogolyubov equations within spherical symmetry. The program HFBRAD (v1.00). Phys. Commun. 168:96-122.
  • Debes, K., Koenig, A., Gross, HM. 2005. Transfer Functions in Artificial Neural Networks. Supplementary Material for urn:nbn:de:0009-3-1515
  • Diamantaras, KI., Kung, SY. 1996. Principal component neural networks: Theory and applications. New York: J. Wiley.
  • Hornik, K., Stinchcombe, M., White, H. 1989. Multilayer freedforward networks are universal approximators. Neural Netw., 89: 359-366.
  • Horný, M. 2014. Bayesian Networks. Technical Report No.5
  • Lalazissis, GA., Raman, S. Ring, P. 1999. Ground-state properties of even-even nuclei in the relativistic mean-field theory. Atom Data Nucl Data, 71: 1–40.
  • Liquid-Drop Model and the Semiempirical Mass Formula. 2018. http://bcs.whfreeman.com/webpub/Ektron/Tipler%20 Modern%20Physics%206e/More%20Sections/More_Chap- ter_11_1-Liquid-Drop_Model_and_the_Semiempirical_ Mass_Formula.pdf.
  • Ring, P. 1996. Relativistic Mean Field Theory in Finite Nuclei. Prog. Part. Nucl. Phys. 37:193-263.
  • Wang, M., Audi, G., Wapstra, AH., Kondev, FG., MacCormick, M., Xu, X., Pfeiffer, B. 2012. The AME2012 atomic mass evaluation. Chin. Phys. C, 36 (2012): 1603–2014.
  • nuclei 36-58Ca, 50-78Ni, 102-138Sn and 182-220Pb by a developed
There are 15 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Hüseyin Aytekin This is me

Publication Date June 1, 2018
Published in Issue Year 2018 Volume: 8 Issue: 2

Cite

APA Aytekin, H. (2018). Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması. Karaelmas Fen Ve Mühendislik Dergisi, 8(2), 602-605.
AMA Aytekin H. Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması. Karaelmas Fen ve Mühendislik Dergisi. June 2018;8(2):602-605.
Chicago Aytekin, Hüseyin. “Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn Ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması”. Karaelmas Fen Ve Mühendislik Dergisi 8, no. 2 (June 2018): 602-5.
EndNote Aytekin H (June 1, 2018) Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması. Karaelmas Fen ve Mühendislik Dergisi 8 2 602–605.
IEEE H. Aytekin, “Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması”, Karaelmas Fen ve Mühendislik Dergisi, vol. 8, no. 2, pp. 602–605, 2018.
ISNAD Aytekin, Hüseyin. “Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn Ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması”. Karaelmas Fen ve Mühendislik Dergisi 8/2 (June 2018), 602-605.
JAMA Aytekin H. Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması. Karaelmas Fen ve Mühendislik Dergisi. 2018;8:602–605.
MLA Aytekin, Hüseyin. “Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn Ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması”. Karaelmas Fen Ve Mühendislik Dergisi, vol. 8, no. 2, 2018, pp. 602-5.
Vancouver Aytekin H. Bir Yapay Sinir Ağı Modeli Yardımıyla 36-58Ca, 50-78Ni, 102-138Sn ve 182-220Pb Çekirdeklerinin İki-Nükleon Ayırma Enerjilerinin Hesaplanması. Karaelmas Fen ve Mühendislik Dergisi. 2018;8(2):602-5.