Earth as an Exoplanet: A Two-dimensional Alien Map

Abstract

Resolving spatially-varying exoplanet features from single-point light curves is essential for determining whether Earth-like worlds harbor geological features and/or climate systems that influence habitability. To evaluate the feasibility and requirements of this spatial feature resolving problem, we present an analysis of multi-wavelength single-point light curves of Earth, where it plays the role of a proxy exoplanet. Here, ~10,000 DSCOVR/EPIC frames collected over a two-year period were integrated over the Earth's disk to yield a spectrallydependent point source and analyzed using singular value decomposition. We found that, between the two dominant principal components (PCs), the second PC contains surface-related features of the planet, while the first PC mainly includes cloud information. We present the first two-dimensional (2D) surface map of Earth reconstructed from light curve observations without any assumptions of its spectral properties. This study serves as a baseline for reconstructing the surface features of Earth-like exoplanets in the future.

Global Mapping of an Exo-Earth Using Sparse Modeling

“…Recently, Fan et al (2019) reproduced the surface map of the Earth using real light-curve observations of ∼10,000 DSCOVR/EPIC frames collected over a two-year period. Since the DSCOVR spacecraft is located at the first Sun-Earth Lagrange point (L1), the current configuration corresponds to ( ) ( ) z =   i , 90 ,23.4 inc with varying Q eq for e O in Equation (3) to be parallel from the Sun to the Earth.…”

“…Observations were taken in 10 optical narrowband channels, and the principal components (PCs) were calculated among all the light curves to extract the surface features. By exploiting the gradient boosting decision tree, Fan et al (2019) found that the second strongest principal component (PC2) traces the surface inhomogeneity of planets. In particular, Fan et al (2019) demonstrated that the time series of PC2 are linearly correlated with those of the overall land fraction, which is the summation of the land fraction weighted by G viewed from the observatory at each phase.…”

“…By exploiting the gradient boosting decision tree, Fan et al (2019) found that the second strongest principal component (PC2) traces the surface inhomogeneity of planets. In particular, Fan et al (2019) demonstrated that the time series of PC2 are linearly correlated with those of the overall land fraction, which is the summation of the land fraction weighted by G viewed from the observatory at each phase. Here, the land fraction is taken from the Global Selfconsistent, Hierarchical, High-resolution Geography Database (Wessel & Smith 1996), and it is shown in Figure 3 We analyze the same data as used in Fan et al (2019).…”

“…Since TESS is equipped with single bandpass, the map recovered by Luger et al (2019b) is largely affected by the cloud reflection. On the other hand, Fan et al (2019) recently recovered a global land map by exploiting multiwavelength light curves of the Earth during the two-year Deep Space Climate Observatory/Earth Polychromatic Imaging Camera (DSCOVR/EPIC) observations (Jiang et al 2018). Using the same data, Kawahara (2020) also reconstructed the geography and spectral components, which reasonably reproduce actual features of the Earth.…”

“…In this paper, we apply sparse modeling to the problem of mapping planetary surfaces from reflectional light in directimaging observations. As fiducial data, we adopted the mock albedo map of the Earth and the real observational data obtained from the Deep Space Climate Observatory (DSCOVR) by Fan et al (2019). For comparison, we solved the mapping problem using both Tikhonov regularization and sparse modeling, and discuss the difference in the output maps.…”