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We present a fully Bayesian approach for the inference of radial profiles ofimpurity transport coefficients and compare its results to neoclassical,gyrofluid and gyrokinetic modeling. Using nested sampling, the BayesianImpurity Transport InferencE (BITE) framework can handle complex parameterspaces with multiple possible solutions, offering great advantages ininterpretative power and reliability with respect to previously demonstratedmethods. BITE employs a forward model based on the pySTRAHL package, built onthe success of the well-known STRAHL code [Dux, IPP Report, 2004], to simulateimpurity transport in magnetically-confined plasmas. In this paper, we focus oncalcium (Ca, Z=20) Laser Blow-Off injections into Alcator C-Mod plasmas.Multiple Ca atomic lines are diagnosed via high-resolution X-ray ImagingCrystal Spectroscopy and Vacuum Ultra-Violet measurements. We analyze asawtoothing I-mode discharge for which neoclassical and turbulent (quasilinearand nonlinear) predictions are also obtained. We find good agreement indiffusion across the entire radial extent, while turbulent convection anddensity profile peaking are estimated to be larger in experiment than suggestedby theory. Efforts and challenges associated with the inference of experimentalpedestal impurity transport are discussed.

Inference of Experimental Radial Impurity Transport on Alcator C-Mod: Bayesian Parameter Estimation and Model Selection