Starshade Exoplanet Data Challenge: What We Learned

Starshade is one of the technologies that will enable the observation and characterization of small planets around nearby stars through direct imaging. Extensive models have been developed to describe a starshade's optical performance and noise budget in exoplanet imaging. The Starshade Exoplanet Data Challenge 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. Because the starshade would suppress the starlight so well, the dominant noise source remaining in the images becomes the exozodiacal disks and their structures. 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, similar to 70% of the inner planets (close to the inner working angle) have been detected along with similar to 30% of the outer planets. Planet detection becomes more difficult in the cases of higher disk inclination as the false negative and false positive counts increase. Interestingly, we found little difference in the planet detection rate between 10(-10) and 10(-9) instrument contrast, confirming that the dominant limitations are from the astrophysical background and not the performance of the starshade. A non-parametric background calibration scheme, such as the independent component analysis reported here, results in a mean residual of 10% the background brightness. This background estimation error leads to substantial false positives and negatives and systematic bias in the planet flux estimation and should be included in the estimation of the planet detection signal-to-noise ratio for imaging using a starshade and also a coronagraph that delivers exozodi-limited imaging. These results corroborate the starshade noise budget and provide new insight into background calibration that will be useful for anticipating the science capabilities of future high-contrast imaging space missions.