FoMo white-light tool: Coupling with state-of-the-art numerical models

Context. Recent advances in observational techniques, for instance, high-resolution white-light images from Solar Orbiter and Parker Solar Probe, as well as numerical modelling approaches to the solar corona, enable us to improve our understanding of solar phenomena. We can use forward modelling to combine our knowledge from observations and modelling. Forward modelling allows for a more thorough analysis of solar phenomena and helps in interpreting observations and improving numerical simulations. Aims. The aim of this work is to develop a new white-light tool to address the need for synthetic data to be generated in a fast and user-friendly way, based on advanced magnetohydrodynamic (MHD) simulations. Methods. We developed a tool that computes the white-light emission as an extension of the FoMo tool. Then, we used the Thomson scattering theory to compute the synthetic data. Results. The FoMo code was extended to produce white-light solar corona images. FoMo is easily coupled with state-of-the-art numerical codes, allowing users to generate synthetic images and videos simply by reading the data directly from the simulations’ snapshots or easily formatted density cubes, while setting user parameters, such as the resolution and position of the virtual spacecraft. The FoMo white-light tool can be applied to a wide range of simulations with minor adjustments to the reading script. We illustrated the application of the FoMo white-light package on two MHD simulations: 3D COCONUT modelling of a coronal mass ejection (CME) propagation in the solar corona and 2.5D ARMVAC modelling of streamer waves. For the latter, we demonstrated that the generated synthetic data was consistent with the observations of the multi-spacecraft observational study of a helmet streamer. Additionally, we evaluated the corrections to the statistical analysis of streamer dynamics after applying the forward modelling FoMo tool to the 2.5D ARMVAC simulation of a streamer wave phenomenon. We show that the discrepancy between the observational and numerical study could be at least partially eliminated by including synthetic data.