Automatic Spatio-Temporal Differential Emission Reconstruction Method

Current analyses of solar differential emission measure predominantly rely on two-dimensional (2D) imaging and interpretation, which inherently limit our ability to fully capture the true three-dimensional (3D) characteristics of coronal structures and dynamic processes. This 2D perspective consequently hinders a comprehensive understanding of the complex physical processes governing the solar atmosphere.

To address these limitations, we present a novel methodology for the spatio-temporal reconstruction of the low solar corona, with several machine learning techniques. This approach enables us to reconstruct several physical parameters, including EUV radiation, temperature, and electron density, across varying altitudes and observation time. Based on these 3D reconstruction results, our method can further generate synthetic observational images from various viewpoints and times, providing a comprehensive visualisation of the corona's dynamic 3D structure. Furthermore, it can estimate missing wavelength observations for missions such as Solar Orbiter. This significantly supports multi-spacecraft collaborative observations and data fusion efforts. Besides, our reconstructed results can also serve as an enhanced initial state for coronal and interplanetary simulations.