Bulg. J. Phys. vol.44 no.4 (2017), pp. 357-361
A Simple Way to Correlate and Predict Neutron Capture Cross Sections Relevant to Astrophysics and to Nuclear Science Applications
A. Couture1, R.F. Casten2,3, R.B. Cakirli4
1Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, USA
2Wright Lab, Yale University, New Haven, Connecticut 06520, USA
3Michigan State University-Facility for Rare Isotope Beams (MSU-FRIB), East Lansing, Michigan 48823, USA
4Department of Physics, University of Istanbul, 34134 Istanbul, Turkey
go back1Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, USA
2Wright Lab, Yale University, New Haven, Connecticut 06520, USA
3Michigan State University-Facility for Rare Isotope Beams (MSU-FRIB), East Lansing, Michigan 48823, USA
4Department of Physics, University of Istanbul, 34134 Istanbul, Turkey
Abstract. Neutron capture cross sections in the keV range are critical for understanding nucleosynthesis in several important astrophysical environments. Certain key cross sections are also of relevance to reactor performance and design and for nuclear forensics. For decades. considerable effort has gone into measuring these cross sections where possible and modeling them where not. Theoretical estimates of unknown cross sections (usually using various statistical models combined with specific structural and reaction input) are often quite uncertain especially when they involve extrapolation to unknown cases. These limitations contribute to ambiguities in understanding various nucleosynthetic processes and delineating the sites where they occur. It is therefore of considerable importance to develop an improved method to correlate known cross sections and to predict new ones with higher accuracy and confidence. Here we present such a method, newly developed, that is simple, empirical, robust, model independent, and based on readily available empirical information. It can provide estimates of unknown cross sections often with accuracies of 20–40%, often for nuclei even quite far from stability, and often converts the estimation process from extrapolation to interpolation.