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

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number78
JournalAstrophysical Journal
Volume911
Issue number2
DOIs
Publication statusPublished - 2021 Apr 20

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this