Bayesian modeling of collisional-radiative models applicable to thermal helium beam plasma diagnostics. (October 2022)

• Record Type: Journal Article
• Authors: Flom, E.; Krychowiak, M.; Schmitz, O.; König, R.; Barbui, T.; Henke, F.; Jakubowski, M.; Kwak, S.; Loch, S.; Muñoz Burgos, J.; Svensson, J.
• Is Part Of: Nuclear materials and energy. Volume 33(2022)
• Publication Date: 2022-10
• Subjects: Bayesian inference -- Collisional-radiative model -- Helium spectroscopy -- Plasma diagnostics; Nuclear energy -- Periodicals; Nuclear fuels -- Periodicals; Nuclear reactors -- Materials -- Periodicals; Radioactive substances -- Periodicals; 621.4833
• DOI: 10.1016/j.nme.2022.101269
• Languages: English
• ISSNs: 2352-1791

Abstract

Abstract: Thermal helium beam diagnostics using the line ratio spectroscopy method are widely used to infer temperature and density in fusion-relevant edge plasmas. These diagnostics consist of an observational system which measures emitted line radiation from either intrinsic or injected helium plasma impurities. These spectral features are then compared to the output of a collisional-radiative model to infer plasma parameters ( T e, n e ) from the observed helium radiation. In order to investigate the systematic uncertainties of such a diagnostic, we present the results of a Bayesian treatment of a helium collisional-radiative model (Schmitz et al., 2008) using synthetic data modeled after an existing system on the plasma experiment Wendelstein 7-X (Barbui et al. 2016). From this study, we present a new method for comprehensively combining measurement uncertainties with underlying atomic rate parameter uncertainties in the inference of plasma parameters. Finally, we also demonstrate the utility of this Bayesian approach in targeting sensitivities within the model, allowing determination of high-priority atomic data for future refinement and comparison between differing atomic models. Highlights: Thermal helium beam diagnostics can measure temperature and density in plasmas. Systematic disagreements motivate a Bayesian treatment of the underlying model. Model uncertainties are large relative to measurement uncertainties. Bayesian technique allows for down-selecting of model data to be refined. 70% of systematic uncertainty comes from uncertainty on 10% of atomic data.