Inference of Experimental Radial Impurity Transport on Alcator C-Mod: Bayesian Parameter Estimation and Model Selection
F. Sciortino, N.T. Howard, E.S. Marmar, T. Odstrcil, N.M. Cao, R. Dux,, A.E. Hubbard, J.W. Hughes, J.H. Irby, Y. Marzouk, L.M. Milanese, M.L. Reinke,, J.E. Rice, P. Rodriguez-Fernandez

TL;DR
This paper introduces a Bayesian framework for inferring impurity transport profiles in tokamak plasmas, demonstrating improved interpretative power and reliability over previous methods through application to Alcator C-Mod calcium injection experiments.
Contribution
The paper develops and applies a Bayesian inference method with nested sampling for impurity transport, integrating it with the pySTRAHL model to analyze experimental data.
Findings
Good agreement in diffusion profiles between experiment and models.
Turbulent convection and density peaking are larger in experiment.
Challenges in inferring pedestal impurity transport are discussed.
Abstract
We present a fully Bayesian approach for the inference of radial profiles of impurity transport coefficients and compare its results to neoclassical, gyrofluid and gyrokinetic modeling. Using nested sampling, the Bayesian Impurity Transport InferencE (BITE) framework can handle complex parameter spaces with multiple possible solutions, offering great advantages in interpretative power and reliability with respect to previously demonstrated methods. BITE employs a forward model based on the pySTRAHL package, built on the success of the well-known STRAHL code [Dux, IPP Report, 2004], to simulate impurity transport in magnetically-confined plasmas. In this paper, we focus on calcium (Ca, Z=20) Laser Blow-Off injections into Alcator C-Mod plasmas. Multiple Ca atomic lines are diagnosed via high-resolution X-ray Imaging Crystal Spectroscopy and Vacuum Ultra-Violet measurements. We analyze a…
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