Bayesian Inference of Nuclear-Matter Density from Proton Scattering
J.C. Zamora

TL;DR
This paper employs Bayesian inference with the Glauber multiple scattering theory to accurately determine nuclear-matter radii from proton elastic scattering data, accounting for uncertainties in input parameters.
Contribution
It introduces a joint Bayesian inference framework that quantifies uncertainties and correlations in input parameters affecting nuclear-matter density extraction.
Findings
Posterior distributions of input parameters were obtained.
Moderate correlation between density parameters and nucleon-nucleon cross sections was identified.
Extracted nuclear-matter radii agree with existing literature.
Abstract
Background: Proton elastic scattering at intermediate energy is widely employed as a tool for determining the matter radius of atomic nuclei. The sensitivity of the approach relies on high-resolution measurements at small scattering angles and low-momentum transfer. Under these conditions, the Glauber multiple scattering theory accurately describes the proton-nucleus elastic cross section. Purpose: Investigate the sensitivity of the Glauber multiple scattering theory to uncertainties associated with input parameters such as the nuclear-matter density distribution and nucleon-nucleon data. Method: A joint Bayesian inference was performed using 12 angular distributions of elastic scattering at different energies on Ni, Zr, and Pb targets. A Metropolis-Hastings algorithm was implemented to make an uncertainty quantification analysis for the input parameters…
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