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Starshade is one of the technologies that will enable the observation and characterization of small planets around solar-like stars through direct imaging. Extensive models have been developed to describe a starshade's optical performance and the resulting noise budget in exoplanet imaging. The Starshade Exoplanetary Data Challenge (SEDC) was designed to validate this noise budget and evaluate the capabilities of image-processing techniques, by inviting community participating teams to analyze >1000 simulated images of hypothetical exoplanetary systems observed through a starshade. One of the biggest challenges of the planetary discovery through the direct image technique is the distinction between true planets and structures in exozodiacal disks. Here we summarize the techniques used by the participating teams and compare their findings with the truth. With an independent component analysis to remove the background, about 70% of the inner planets (close to the inner working angle) have been detected and ~40 of the outer planet (fainter than the inner counterparts) have been identified. Also, the inclination of the exozodiacal disk can be inferred from individual images. Planet detection becomes more difficult in the cases of higher disk inclination, as the false negative and false positive numbers increase. Finally, we find that a non-parametric background calibration scheme, such as the independent component analysis reported here, can perform background subtraction close to the photon-noise limit, with a median residual of ~5% the background brightness, for exozodiacal density level ranging between 1 and 30 zodis. The results of the SEDC strongly corroborate the starshade noise budget with realistic images, and provide new insight into background calibration that will be useful for anticipating the science capabilities of future missions that use a starshade.
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