Constraining Transfer Cross Sections Using Bayes' Theorem
A. E. Lovell, F. M. Nunes

TL;DR
This paper applies Bayesian methods with Markov Chain Monte Carlo to quantify uncertainties in nuclear transfer reaction models, demonstrating how experimental error reduction and model choice influence the precision of transfer cross section predictions.
Contribution
It introduces a Bayesian framework for constraining optical potentials and transfer predictions, highlighting the impact of experimental errors and model differences on uncertainties.
Findings
Uncertainties in transfer cross sections range from 25% to 100%.
Reducing experimental errors decreases uncertainties but not proportionally.
Adiabatic model yields smaller uncertainties than DWBA.
Abstract
Being able to rigorously quantify the uncertainties in reaction models is crucial to moving this field forward. Even though Bayesian methods are becoming increasingly popular in nuclear theory, they are yet to be implemented and applied in reaction theory problems. The purpose of this work is to investigate, using Bayesian methods, the uncertainties in the optical potentials generated from fits to elastic scattering data and the subsequent uncertainties in the transfer predictions. We also study the differences in two reaction models where the parameters are constrained in a similar manner, as well as the impact of reducing the experimental error bars on the data used to constrain the parameters. We use Bayes' Theorem combined with a Markov Chain Monte Carlo to determine posterior distributions for the parameters of the optical model, constrained by neutron-, proton-, and/or…
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