[P-AE16] Exoplanets

[PAE16-03] Global Mapping of the Surface Composition on an Exo-Earth using Sparse Modeling

*Atsuki Kuwata¹, Hajime Kawahara¹, Masataka Aizawa², Takayuki Kotani^3,4,5, Motohide Tamura^1,3,4 (1.The University of Tokyo, 2.Tsung-Dao Lee Institute, 3.Astrobiology center, 4.National Astronomical Observatory of Japan, 5.University for Advanced Studies (SOKENDAI))

Keywords:Exoplanets, Mapping, Sparse Modeling

In the exploration of extraterrestrial life, the characterization of exoplanets has been studied. Direct imaging is one of the fundamental observation methods for characterization. However, even for close terrestrial exoplanets from the solar system, a resolution of a few microarcseconds is required for spatially resolved observations, and even if direct imaging becomes possible, it will only be possible to observe point sources. Kawahara & Fujii (2010, 2011) proposed Spin-Orbit Tomography (SOT) to obtain the two-dimensional spatial distribution of a planet's surface from the temporal variation of observed reflected light without spatial resolution. Recently, Aizawa et al. (2020) obtained highly accurate spatial distribution maps by introducing sparse modeling to SOT.

In addition to the above methods, we focused on Spin-Orbit Unmixing (SOU), which was developed by introducing Spectral Unmixing, a remote sensing method, to SOT. SOU enables us to obtain the spatial distribution of the planet's surface and the reflection spectrum from the luminosity variations at multiple wavelengths (Kawahara 2020). In this study, we introduced sparse modeling to SOU. In SOU, we placed sparsity-inducing constraints and reformulated the solution into an appropriate form to use the proximal gradient method, one of the optimization algorithms. As a result, we obtain highly accurate and more sparse planetary surface distributions and reflection spectra. In this talk, we describe this method and show the results of tests using Earth data.