An Approximate Bayesian Deep Learning Approach for Uncertainty-aware Differential Emission Measure Estimates in the Solar Corona from the SDO
Accurately estimating the temperature distribution of solar coronal plasma, known as the Differential Emission Measure (DEM), is vital for understanding the thermodynamics of the corona and associated heating. However, recovering the DEM from multispectral observations like those from the Atmospheric Imaging Assembly (AIA) on board NASA's Solar Dynamics Observatory (SDO) is a mathematically ill-posed, underdetermined problem, and traditional regularization-based inversion methods are computationally intensive and provide limited uncertainty quantification. We present a deep learning framework for DEM reconstruction that incorporates Monte Carlo Dropout to perform approximate Bayesian inference, yielding per-pixel empirical distributions over the DEM that characterize epistemic or systematic model uncertainty in the learned inversion. The network is trained with a dual-head architecture supervising both AIA image reconstruction and DEM fidelity, with non-negativity enforced by construction. The network is trained directly on real SDO/AIA observations along with the associated DEM solutions from regularized inversion. We validate performance against both synthetic thermal distributions and real coronal data, demonstrating accurate recovery of thermal structure across a range of plasma conditions, while maintaining significant computational efficiency over traditional inversion techniques. This method ensures physically legitimate, non-negative solutions and provides per-pixel uncertainties, making it a reliable, high-speed, and uncertainty-aware tool for large-scale solar data analysis.