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The ExoEarth Yield Landscape for Future Direct Imaging Space Telescopes

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 نشر من قبل Christopher Stark
 تاريخ النشر 2019
  مجال البحث فيزياء
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The expected yield of potentially Earth-like planets is a useful metric for designing future exoplanet-imaging missions. Recent yield studies of direct-imaging missions have focused primarily on yield methods and trade studies using toy models of missions. Here we increase the fidelity of these calculations substantially, adopting more realistic exoplanet demographics as input, an improved target list, and a realistic distribution of exozodi levels. Most importantly, we define standardized inputs for instrument simulations, use these standards to directly compare the performance of realistic instrument designs, include the sensitivity of coronagraph contrast to stellar diameter, and adopt engineering-based throughputs and detector parameters. We apply these new high-fidelity yield models to study several critical design trades: monolithic vs segmented primary mirrors, on-axis vs off-axis secondary mirrors, and coronagraphs vs starshades. We show that as long as the gap size between segments is sufficiently small, there is no difference in yield for coronagraph-based missions with monolithic off-axis telescopes and segmented off-axis telescopes, assuming that the requisite engineering constraints imposed by the coronagraph can be met in both scenarios. We show that there is currently a factor of ~2 yield penalty for coronagraph-based missions with on-axis telescopes compared to off-axis telescopes, and note that there is room for improvement in coronagraph designs for on-axis telescopes. We also reproduce previous results in higher fidelity showing that the yields of coronagraph-based missions continue to increase with aperture size while the yields of starshade-based missions turnover at large apertures if refueling is not possible. Finally, we provide absolute yield numbers with uncertainties that include all major sources of astrophysical noise to guide future mission design.

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