Neutron capture cross sections from surrogate reaction data and theory: connecting the pieces with a Markov-Chain Monte Carlo approach

TL;DR

This paper enhances the determination of neutron capture cross sections by applying a sophisticated Markov Chain Monte Carlo method to surrogate reaction data, improving upon previous Bayesian sampling techniques.

Contribution

It introduces a Markov Chain Monte Carlo approach for analyzing surrogate reaction data to accurately determine neutron capture cross sections, advancing the methodology in nuclear reaction analysis.

Findings

Preliminary results demonstrate the effectiveness of the MCMC method.

The approach provides a more robust analysis compared to previous Bayesian sampling.

Potential for improved accuracy in neutron cross section measurements.

Abstract

The neutron capture cross section for $^{90}Zr(n, \gamma)$ has recently been determined using surrogate $^{92}Zr(p, d\gamma)$ data and nuclear reaction theory. That work employed an approximate fitting method based on Bayesian Monte Carlo sampling to determine parameters needed for calculating the $^{90}Zr(n, \gamma)$ cross section. Here, we improve the approach by introducing a more sophisticated Markov Chain Monte Carlo sampling method. We present preliminary results.