MCMC Surrogate Method
231
the joint probability distribution of parameters, including correlations between the
parameters. The result is seen to be in agreement with the TENDL 2015 and
ENDF/B-VII.1 evaluations, both of which are based on directly measured data.
In future work we will investigate correlations between the model parameters and
between the cross sections at different energies.
Acknowledgments This work was performed under the auspices of the U.S. Department of
Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344, with
support from LLNL’s HEDP summer student program and LDRD project 19-ERD-017.
References
1. R. Capote et al., RIPL - reference input parameter library for calculation of nuclear reactions
and nuclear data evaluations. Nucl. Data Sheets 110, 3107 (2009)
2. J.E. Escher et al., Compound-nuclear reaction cross sections from surrogate measurements. Rev.
Mod. Phys. 84, 353 (2012)
3. J.E. Escher et al., Constraining neutron capture cross sections for unstable nuclei with surrogate
reaction data and theory. Phys. Rev. Lett. 121, 052501 (2018)
4. A. Gilbert, A. Cameron, A composite nuclear-level density formula with shell corrections. Can.
J. Phys. 43(8), 1446–1496 (1965)
5. A. Ignatyuk, et al., Sov. J. Nucl. Phys. 21, 255 (1975)
6. W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications.
Biometrika 57, 97 (1970)
7. D.P. Kroese, T. Taimre, Z.I. Botev, Handbook of Monte Carlo Methods. Wiley Series in
Probability and Statistics (Wiley, New York, 2011)
8. R. Koning, Modern nuclear data evaluation with the TALYS code system. Nucl. Data Sheets
113, 2841 (2012)
9. M.B. Chadwick et al., ENDF/B-VII.0: Next generation evaluated nuclear data library for nuclear
science and technology. Nucl. Data Sheets 107, 2931 (2006)
231
the joint probability distribution of parameters, including correlations between the
parameters. The result is seen to be in agreement with the TENDL 2015 and
ENDF/B-VII.1 evaluations, both of which are based on directly measured data.
In future work we will investigate correlations between the model parameters and
between the cross sections at different energies.
Acknowledgments This work was performed under the auspices of the U.S. Department of
Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344, with
support from LLNL’s HEDP summer student program and LDRD project 19-ERD-017.
References
1. R. Capote et al., RIPL - reference input parameter library for calculation of nuclear reactions
and nuclear data evaluations. Nucl. Data Sheets 110, 3107 (2009)
2. J.E. Escher et al., Compound-nuclear reaction cross sections from surrogate measurements. Rev.
Mod. Phys. 84, 353 (2012)
3. J.E. Escher et al., Constraining neutron capture cross sections for unstable nuclei with surrogate
reaction data and theory. Phys. Rev. Lett. 121, 052501 (2018)
4. A. Gilbert, A. Cameron, A composite nuclear-level density formula with shell corrections. Can.
J. Phys. 43(8), 1446–1496 (1965)
5. A. Ignatyuk, et al., Sov. J. Nucl. Phys. 21, 255 (1975)
6. W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications.
Biometrika 57, 97 (1970)
7. D.P. Kroese, T. Taimre, Z.I. Botev, Handbook of Monte Carlo Methods. Wiley Series in
Probability and Statistics (Wiley, New York, 2011)
8. R. Koning, Modern nuclear data evaluation with the TALYS code system. Nucl. Data Sheets
113, 2841 (2012)
9. M.B. Chadwick et al., ENDF/B-VII.0: Next generation evaluated nuclear data library for nuclear
science and technology. Nucl. Data Sheets 107, 2931 (2006)