2024/03/30 by Seung Whan Chung, Todd Oliver, Chung, Seung Whan +5
Decision Sciences · Engineering · Materials Science · #Data Analysis #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #Plasma Diagnostics and Applications #Plasma Physics (physics.plasm-ph) #Scientific Measurement and Uncertainty Evaluation #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2404.00467
openalex publication_date 2024/03/30 · openalex created_date 2024/04/04 · openalex updated_date 2026/07/28
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.