Gaussian process regression for thermal transport analysis in infrared imaging video bolometry

Accurate characterization of thermal diffusion and blackbody radiation on the detector foil is crucial in infrared imaging video bolometry (IRVB) for reliably inferring the spatial distribution of plasma radiation. This paper presents a new inference framework for modeling blackbody radiation and thermal diffusion power densities using Gaussian process regression. This method is validated with both synthetic and experimental IRVB data, producing reliable results without the need for temporal or spatial averaging. In addition, the effects of noise level and foil material are analyzed, and both the limitations of this framework and strategies for improving its performance are identified.