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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 0.016 Z Partly based on data obtained with the Subaru Telescope. The Subaru Telescope is operated by the National Astronomical Observatory of Japan. † † The data presented herein were partly obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute

We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (∼500 deg2) optical images reaching a 5 limit of ∼26 mag, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To select Z/Z < 0.1 EMPGs from ∼40 million sourc...

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Published in:The Astrophysical journal 2020-08, Vol.898 (2)
Main Authors: Kojima, Takashi, Ouchi, Masami, Rauch, Michael, Ono, Yoshiaki, Nakajima, Kimihiko, Isobe, Yuki, Fujimoto, Seiji, Harikane, Yuichi, Hashimoto, Takuya, Hayashi, Masao, Komiyama, Yutaka, Kusakabe, Haruka, Kim, Ji Hoon, Lee, Chien-Hsiu, Mukae, Shiro, Nagao, Tohru, Onodera, Masato, Shibuya, Takatoshi, Sugahara, Yuma, Umemura, Masayuki, Yabe, Kiyoto
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container_title The Astrophysical journal
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creator Kojima, Takashi
Ouchi, Masami
Rauch, Michael
Ono, Yoshiaki
Nakajima, Kimihiko
Isobe, Yuki
Fujimoto, Seiji
Harikane, Yuichi
Hashimoto, Takuya
Hayashi, Masao
Komiyama, Yutaka
Kusakabe, Haruka
Kim, Ji Hoon
Lee, Chien-Hsiu
Mukae, Shiro
Nagao, Tohru
Onodera, Masato
Shibuya, Takatoshi
Sugahara, Yuma
Umemura, Masayuki
Yabe, Kiyoto
description We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (∼500 deg2) optical images reaching a 5 limit of ∼26 mag, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To select Z/Z < 0.1 EMPGs from ∼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 at z = 0.007-0.03 with large Hβ equivalent widths of 104-265 , stellar masses of log( / ) = 5.0-7.1, and high specific star formation rates of ∼300 Gyr−1, which are similar to those of early galaxies at z 6 reported recently. We spectroscopically confirm that 3 out of 10 candidates are truly EMPGs with Z/Z < 0.1, one of which is HSC J1631+4426, the most metal-poor galaxy, with Z/Z = 0.016, ever reported.
doi_str_mv 10.3847/1538-4357/aba047
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To select Z/Z &lt; 0.1 EMPGs from ∼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 at z = 0.007-0.03 with large Hβ equivalent widths of 104-265 , stellar masses of log( / ) = 5.0-7.1, and high specific star formation rates of ∼300 Gyr−1, which are similar to those of early galaxies at z 6 reported recently. 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I. A Successful Machine-learning Selection of Metal-poor Galaxies and the Discovery of a Galaxy with M &lt; 106 M and 0.016 Z Partly based on data obtained with the Subaru Telescope. The Subaru Telescope is operated by the National Astronomical Observatory of Japan. † † The data presented herein were partly obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute</atitle><jtitle>The Astrophysical journal</jtitle><stitle>APJ</stitle><addtitle>Astrophys. J</addtitle><date>2020-08-03</date><risdate>2020</risdate><volume>898</volume><issue>2</issue><issn>0004-637X</issn><eissn>1538-4357</eissn><abstract>We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (∼500 deg2) optical images reaching a 5 limit of ∼26 mag, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To select Z/Z &lt; 0.1 EMPGs from ∼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 at z = 0.007-0.03 with large Hβ equivalent widths of 104-265 , stellar masses of log( / ) = 5.0-7.1, and high specific star formation rates of ∼300 Gyr−1, which are similar to those of early galaxies at z 6 reported recently. 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subjects Emission nebulae
Galaxy abundances
Galaxy evolution
Galaxy formation
Interstellar medium
title 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 0.016 Z Partly based on data obtained with the Subaru Telescope. The Subaru Telescope is operated by the National Astronomical Observatory of Japan. † † The data presented herein were partly obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute
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