Uncertainty quantification for optical model parameters

Lovell, A. E.; Nunes, F. M.; Sarich, J.; Wild, S. M.

doi:10.1103/PhysRevC.95.024611

Title: Uncertainty quantification for optical model parameters

Journal Article · Tue Feb 21 00:00:00 EST 2017 · Physical Review C

DOI:https://doi.org/10.1103/PhysRevC.95.024611· OSTI ID:1357088

Lovell, A. E. ^[1]; Nunes, F. M. ^[1]; Sarich, J. ^[2]; Wild, S. M. ^[2]

Michigan State Univ., East Lansing, MI (United States). National Superconducting Cyclotron Lab. Dept. of Physics and Astronomy
Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division

Background: Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. Purpose: The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. Method: We use statistical methods to determine a best fit and create corresponding 95% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. Results: We study a number of reactions involving neutron and deuteron projectiles with energies in the range of 5-25 MeV/u, on targets with mass A = 12-208. We investigate the correlations between the parameters in the fit. The case of deuterons on C-12 is discussed in detail: the elastic-scattering fit and the prediction of C-12(d, p) C-13 transfer angular distributions, using both uncorrelated and correlated. 2 minimization functions. The general features for all cases are compiled in a systematic manner to identify trends. Conclusions: Our work shows that, in many cases, the correlated chi(2) functions (in comparison to the uncorrelated chi(2) functions) provide a more natural parameterization of the process. These correlated functions do, however, produce broader confidence bands. Further optimization may require improvement in the models themselves and/or more information included in the fit.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Michigan State Univ., East Lansing, MI (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)

Grant/Contract Number:: NA0002135; FG52-08NA28552; AC02-06CH11357; PHY-1403906; PHY-1520929

OSTI ID:: 1357088

Alternate ID(s):: OSTI ID: 1344597; OSTI ID: 1368560

Journal Information:: Physical Review C, Vol. 95, Issue 2; ISSN 2469-9985

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 25 works

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