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Reconstructing an image from sparsely sampled Fourier data is an ill-posedinverse problem that occurs in a variety of subjects within science, includingthe data analysis for Very Long Baseline Interferometry (VLBI) and theSpectrometer/Telescope for Imaging X-rays (STIX) for solar observations.Despite ongoing parallel developments of novel imaging algorithms, synergiesremain unexplored. We study the synergies between the data analysis for theSTIX instrument and VLBI, compare the methodologies and evaluate theirpotential. In this way, we identify key trends in the performance of severalalgorithmic ideas and draw recommendations for the future. To this end, weorganized a semi-blind imaging challenge with data sets and source structuresthat are typical for sparse VLBI, specifically in the context of the EventHorizon Telescope (EHT), and for STIX observations. 17 different algorithmsfrom both communities, from 6 different imaging frameworks, participated in thechallenge, marking this work the largest scale code comparisons for STIX andVLBI to date. Strong synergies between the two communities have beenidentified, as can be proven by the success of the imaging methods proposed forSTIX in imaging VLBI data sets and vice versa. Novel imaging methods outperformthe standard CLEAN algorithm significantly in every test-case. Improvementsover the performance of CLEAN make deeper updates to the inverse modelingpipeline necessary, or consequently replacing inverse modeling with forwardmodeling. Entropy-based and Bayesian methods perform best on STIX data. Themore complex imaging algorithms utilizing multiple regularization terms(recently proposed for VLBI) add little to no additional improvements for STIX,but outperform the other methods on EHT data. This work demonstrates the greatsynergy between the STIX and VLBI imaging efforts and the great potential forcommon developments.

Identifying synergies between VLBI and STIX imaging