Silverrush X: Machine learning-aided selection of 9318 LAEs at z=2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS survey data

Yoshiaki Ono, Ryohei Itoh, Takatoshi Shibuya, Masami Ouchi, Yuichi Harikane, Satoshi Yamanaka, Akio K. Inoue, Toshiyuki Amagasa, Daichi Miura, Maiki Okura, Kazuhiro Shimasaku, Yoshiaki Taniguchi, Seiji Fujimoto, Masanori Iye, Anton T. Jaelani, Ikuru Iwata, Nobunari Kashikawa, Shotaro Kikuchihara, Satoshi Kikuta, Masakazu A.R. KobayashiHaruka Kusakabe, Chien Hsiu Lee, Yongming Liang, Yoshiki Matsuoka, Rieko Momose, Tohru Nagao, Kimihiko Nakajima, Ken Ichi Tadaki

研究成果査読

1 被引用数 (Scopus)

抄録

We present a new catalog of 9318 Lyα emitter (LAE) candidates at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to 25.0 deg2) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru. We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of 94% and a contamination rate of 1%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include 177 LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are;80%–100% at bright NB magnitudes of ≲24 mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage.

本文言語English
論文番号78
ジャーナルAstrophysical Journal
911
2
DOI
出版ステータスPublished - 2021 4 20

ASJC Scopus subject areas

  • 天文学と天体物理学
  • 宇宙惑星科学

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