  The predictive capability of a plasma discharge model depends on accurate
representations of electron-impact collision cross sections, which determine
the key reaction rates and transport properties of the plasma. Although many
cross sections have been identified through experiments and quantum mechanical
simulations, their uncertainties are not well-investigated. We characterize the
uncertainties in electron-argon collision cross sections using a Bayesian
framework. Six collision processes -- elastic momentum transfer, ionization,
and four excitations -- are characterized with semi-empirical models, whose
parametric uncertainties effectively capture the features important to the
macroscopic properties of the plasma, namely transport properties and chemical
reaction rates. The method is designed to capture the effects of systematic
errors that lead to large discrepancies between some data sets. Specifically,
for the purposes of Bayesian inference, each of the parametric cross section
models is augmented with a Gaussian process representing systematic measurement
errors as well as model inadequacies in the parametric form. The results show
that the method is able to capture scatter in the data between the
electron-beam experiments and ab-initio quantum simulations. The calibrated
cross section models are further validated against measurements from
swarm-parameter experiments.
