Although the faces of extrasolar planets will not be spatially-resolved in the foreseeable future, their rotational and orbital motions produce detectable changes in color and brightness. Ford, Seager & Turner (2001) used simulations of Earth to show that the changing colors of its disk-integrated reflected light encode information about continents, oceans, and clouds. The inverse problem—inferring the surface geography of a planet based on time-resolved photometry—is much more daunting than the forward problem and at first blush looked intractable. However, cracking the “exo-cartography” inverse problem is a worthy challenge because it offers important clues about planet formation, evolution, and climate—foremost areas of research in planetary science. This is also a timely problem, because reflected light from short-period planets can now be measured, and high-contrast imaging projects are poised to extend this approach to cooler planets.
In recent years, progress in exo-cartography has been made by taking on small parts of the problem in isolation. Members of our team have demonstrated how disk-integrated reflected light curves can be analyzed to obtain a planet’s rotation rate, spin-orientation, surface geography, cloud distribution, and spectra of surfaces. These successes have led exo-cartography to be a primary science driver of next-generation direct-imaging missions (NY Times, 17/02/2014). But the full inverse problem is more challenging than the sum of its parts and that is what our ISSI International Team proposes to tackle.
Our team will take a huge step forward by combining the major advances of the last decade. We will 1) construct a suite of full-physics simulated reflected lightcurves of exoplanets using the Virtual Planetary Laboratory’s Earth model, and 2) attempt to infer the planetary properties in a blind fashion by analyzing these lightcurves with a variety of inverse methods including a Markov Chain Monte Carlo. We will also set intermediate “data challenges” to better understand which assumptions are most critical to the retrieval exercise. The results of our collaboration will be published in one or more peer-reviewed papers, and our lightcurves and inverse models will be made publicly available to serve as a benchmark for future efforts.