Starshade Exoplanet Data Challenge: What We Learned

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. In this paper, 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 $\sim$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 counts increase. Finally, we find that a non-parametric background calibration scheme, such as the independent component analysis reported here, results in a mean residual of $5-15\%$ the background brightness, and this background estimation error leads to substantial false positives and negatives and systematic bias in the planet flux estimation. The results of the SEDC 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 high-contrast imaging space missions.