Extremely Metal-poor Representatives Explored by the Subaru Survey (EMPRESS). I. A Successful Machine-learning Selection of Metal-poor Galaxies and the Discovery of a Galaxy with M* < 106 M⊙ and 0.016Z⊙

We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (similar to 500 deg(2)) optical images reaching a 5 sigma limit of similar to 26 mag, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To selectZ/Z< 0.1 EMPGs from similar to 40 million sources detected in the Subaru images, we first develop a machine-learning (ML) classifier based on a deep neural network algorithm with a training data set consisting of optical photometry of galaxy, star, and QSO models. We test our ML classifier with SDSS objects having spectroscopic metallicity measurements and confirm that our ML classifier accomplishes 86% completeness and 46% purity EMPG classifications with photometric data. Applying our ML classifier to the photometric data of the Subaru sources, as well as faint SDSS objects with no spectroscopic data, we obtain 27 and 86 EMPG candidates from the Subaru and SDSS photometric data, respectively. We conduct optical follow-up spectroscopy for 10 of our EMPG candidates with Magellan/LDSS-3+MagE, Keck/DEIMOS, and Subaru/FOCAS and find that the 10 EMPG candidates are star-forming galaxies atz = 0.007-0.03 with large H beta equivalent widths of 104-265 A, stellar masses of log(