Nuclear Mass Model Based on Bayesian Estimate of Local Difference Experssions of Binding Energies

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The Bayesian estimates of the value of the residual neutron–proton interaction energy Δnp  using the Markov chain Monte Carlo method and Tikhonov regularization. These estimates are used for calculation of the nuclear mass table for A > 20. The accuracy of the obtained predictions is evaluated by comparison with experimental data from AME2020 and other theoretical nuclear mass models.

About the authors

K. A. Stopani

Skobeltsyn Institute of Nuclear Physics, Moscow State University

Email: kstopani@sinp.msu.ru
Moscow, Russia

E. V. Vladimirova

Faculty of Physics, Moscow State University

Email: kstopani@sinp.msu.ru
Moscow, Russia

V. V. Negrebetskiy

Faculty of Physics, Moscow State University; Justus Liebig University

Email: kstopani@sinp.msu.ru
Moscow, Russia; Giessen, Germany

M. V. Simonov

Faculty of Physics, Moscow State University; Justus Liebig University

Email: kstopani@sinp.msu.ru
Moscow, Russia; Giessen, Germany

T. Yu. Tretyakova

Skobeltsyn Institute of Nuclear Physics, Moscow State University; Faculty of Physics, Moscow State University

Author for correspondence.
Email: kstopani@sinp.msu.ru
Moscow, Russia; Moscow, Russia

References

  1. G. T. Garvey, W. J. Gerace, R. L. Jaffe, I. Talmi, and I. Kelson, Rev. Mod. Phys. 41, S1 (1969).
  2. С. Шапиро, С. Тьюколски, Чёрные дыры, белые карлики и нейтронные звёзды, ч. 1 (Мир, Москва, 1985).
  3. J. Jänecke and H. Behrens, Phys. Rev. C 9, 1276 (1974).
  4. J. Jänecke and P. J. Masson, At. Data Nucl. Data Tables 39, 265 (1988).
  5. P. J. Masson and J. Jänecke, At. Data Nucl. Data Tables 39, 273 (1988).
  6. Е. В. Владимирова, Б. С. Ишханов, М. В. Симонов, Т. Ю. Третьякова, Уч. записки физ. фак-та Моск. ун-та, 1930409 (2019).
  7. E. V. Vladimirova, B. S. Ishkhanov, M. V. Simonov, S. V. Sidorov, and T. Yu Tretyakova, Int. J. Mod. Phys. E 30, 2150025 (2021).
  8. E. V. Vladimirova, M. V. Simonov, and T. Yu. Tre- tyakova, AIP Conf. Proc. 2377, 070003 (2021).
  9. D. Lunney, J. M. Pearson, and C. Thibault, Rev. Mod. Phys. 75, 1021 (2003).
  10. Z. He, M. Bao, Y. M. Zhao, and A. Arima, Phys. Rev. C 90, 054320 (2014).
  11. J. L. Tian, N. Wang, C. Li, and J. J. Li, Phys. Rev. C 87, 014313 (2013).
  12. Y. Y. Cheng, Y. M. Zhao, and A. Arima, Phys. Rev. C 89, 061304 (2014).
  13. W. J. Huang, G. Audi, M. Wang, F. G. Kondev, S. Naimi, and X. Xu, Chin. Phys. C 41, 030002 (2017); M. Wang, G. Audi, F. G. Kondev, W. J. Huang, S. Naimi, and X. Xu, Chin. Phys. C 41, 030003 (2017).
  14. А. А. Боровков, Математическая статистика (Лань, Cанкт-Петербург, 2010).
  15. А. Н. Тихонов, Докл. Акад. Наук СССР 151, 501 (1963).
  16. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dun- son, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, 3rd ed. (New York, Chapman and Hall/CRC, 2013).
  17. R. M. Neal, Tech. Report CRG-TR-93-1 (University of Toronto, September 1993).
  18. W. J. Huang, M. Wang, F. G. Kondev, G. Audi, and S. Naimi, Chin. Phys. C 45, 030002 (2021); M. Wang, W. J. Huang, F. G. Kondev, G. Audi, and S. Naimi, Chin. Phys. C 45, 030003 (2021).
  19. P. Möller, A. J. Sierk, T. Ichikawa, and H. Sagawa, At. Data Nucl. Data Tables 109, 1 (2016).
  20. S. Goriely, N. Chamel, and J. M. Pearson, Phys. Rev. Lett. 102, 152503 (2009).
  21. A. Pastore, D. Neill, H. Powell, K. Medler, and C. Barton, Phys. Rev. C 101, 035804 (2020).
  22. J. Duflo and A. P. Zuker, Phys. Rev. C 52, R23 (1995).
  23. M. Shelley and A. Pastore, Universe 7, 131 (2021).
  24. L. Neufcourt, Y. Cao, W. Nazarewicz, and F. Viens, Phys. Rev. C 98, 034318 (2018).
  25. N.-N. Ma, H.-F. Zhang, X.-J. Bao, and H.-F. Zhang, Chin. Phys. C 43, 044105 (2019).
  26. A. Gration and M. I. Wilkinson, Mon. Not. Roy. Astron. Soc. 485, 4878 (2019).

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2023 Pleiades Publishing, Ltd.