Global Mapping of Surface Composition on an Exo-Earth Using Sparse Modeling

The time series of light reflected from exoplanets by future direct imaging can provide spatial information with respect to the planetary surface. We apply sparse modeling to the retrieval method that disentangles the spatial and spectral information from multi-band reflected light curves termed as spin-orbit unmixing. We use the $\ell_1$-norm and the Total Squared Variation norm as regularization terms for the surface distribution. Applying our technique to a toy model of cloudless Earth, we show that our method can infer sparse and continuous surface distributions and also unmixed spectra without prior knowledge of the planet surface. We also apply the technique to the real Earth data as observed by DSCOVR/EPIC. We determined the representative components that can be interpreted as cloud and ocean. Additionally, we found two components that resembled the distribution of land. One of the components captures the Sahara Desert, and the other roughly corresponds to vegetation although their spectra are still contaminated by clouds. Sparse modeling significantly improves the geographic retrieval, in particular, of cloud and leads to higher resolutions for other components when compared with spin-orbit unmixing using Tikhonov regularization.Comment: 26 pages, 10 figures, accepted for publication in Ap

GALAXY CRUISE: Deep Insights into Interacting Galaxies in the Local Universe

We present the first results from GALAXY CRUISE, a community (or citizen) science project based on data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The current paradigm of galaxy evolution suggests that galaxies grow hierarchically via mergers, but our observational understanding of the role of mergers is still limited. The data from HSC-SSP are ideally suited to improve our understanding with improved identifications of interacting galaxies thanks to the superb depth and image quality of HSC-SSP. We have launched a community science project, GALAXY CRUISE, in 2019 and collected over 2 million independent classifications of 20,686 galaxies at z < 0.2. We first characterize the accuracy of the participants' classifications and demonstrate that it surpasses previous studies based on shallower imaging data. We then investigate various aspects of interacting galaxies in detail. We show that there is a clear sign of enhanced activities of super massive black holes and star formation in interacting galaxies compared to those in isolated galaxies. The enhancement seems particularly strong for galaxies undergoing violent merger. We also show that the mass growth rate inferred from our results is roughly consistent with the observed evolution of the stellar mass function. The 2nd season of GALAXY CRUISE is currently under way and we conclude with future prospects. We make the morphological classification catalog used in this paper publicly available at the GALAXY CRUISE website, which will be particularly useful for machine-learning applications.Comment: 23 pages, 22 figures, PASJ in press. Data available at https://galaxycruise.mtk.nao.ac.jp/en/for_researchers.htm