SuNeRF-CME: Physics-informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections

Abstract Coronagraphic observations enable direct monitoring of coronal mass ejections (CMEs) through scattered light from free electrons, but determining the 3D plasma distribution from 2D imaging data is challenging due to the optically thin plasma and the complex image formation. We introduce Sun Neural Radiance Field for CMEs (SuNeRF-CME), a framework for 3D tomographic reconstructions of the heliosphere using multiviewpoint coronagraphic observations. The method uses a neural radiance-field to estimate the electron density in the heliosphere through ray tracing, while accounting for the underlying Thomson scattering. The model is optimized by iteratively fitting the time-dependent observational data. In addition, we apply physical constraints in terms of continuity, propagation direction, and speed of the heliospheric plasma to overcome limitations imposed by the sparse number of viewpoints. We utilize synthetic observations of a CME simulation to quantify the model’s performance for different viewpoint configurations. Within this controlled synthetic setting, the results demonstrate that our method can reliably estimate the CME parameters from only two viewpoints, with a mean velocity error of 3.01% ± 1.94% and propagation direction errors of 3 . ° 39 ± 1 . ° 94 in latitude and 1 . ° 76 ± 0 . ° 79 in longitude. We further show that our approach can achieve a 3D reconstruction of the simulated CME from two viewpoints, where we correctly model the three-part structure, deformed CME front, and internal plasma variations. Additional viewpoints can be seamlessly integrated, directly enhancing the reconstruction of the plasma distribution in the heliosphere. These results demonstrate the potential of physics-informed radiance-field methods for CME tomography, paving the way for future extensions toward observational data and space weather applications.