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

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Standard

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

In: Astrophysical Journal, Vol. 911, No. 2, 78, 09.04.2021.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ono, Y, Itoh, R, Shibuya, T, Ouchi, M, Harikane, Y, Yamanaka, S, Inoue, AK, Amagasa, T, Miura, D, Okura, M, Shimasaku, K, Iwata, I, Taniguchi, Y, Fujimoto, S, Iye, M, Jaelani, AT, Kashikawa, N, Kikuchihara, S, Kikuta, S, Kobayashi, MAR, Kusakabe, H, Lee, C-H, Liang, Y, Matsuoka, Y, Momose, R, Nagao, T, Nakajima, K & Tadaki, K 2021, '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', Astrophysical Journal, vol. 911, no. 2, 78. https://doi.org/10.3847/1538-4357/abea15

APA

Ono, Y., Itoh, R., Shibuya, T., Ouchi, M., Harikane, Y., Yamanaka, S., Inoue, A. K., Amagasa, T., Miura, D., Okura, M., Shimasaku, K., Iwata, I., Taniguchi, Y., Fujimoto, S., Iye, M., Jaelani, A. T., Kashikawa, N., Kikuchihara, S., Kikuta, S., ... Tadaki, K. (2021). 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. Astrophysical Journal, 911(2), [78]. https://doi.org/10.3847/1538-4357/abea15

Vancouver

Ono Y, Itoh R, Shibuya T, Ouchi M, Harikane Y, Yamanaka S et al. 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. Astrophysical Journal. 2021 Apr 9;911(2). 78. https://doi.org/10.3847/1538-4357/abea15

Author

Ono, Yoshiaki ; Itoh, Ryohei ; Shibuya, Takatoshi ; Ouchi, Masami ; Harikane, Yuichi ; Yamanaka, Satoshi ; Inoue, Akio K. ; Amagasa, Toshiyuki ; Miura, Daichi ; Okura, Maiki ; Shimasaku, Kazuhiro ; Iwata, Ikuru ; Taniguchi, Yoshiaki ; Fujimoto, Seiji ; Iye, Masanori ; Jaelani, Anton T. ; Kashikawa, Nobunari ; Kikuchihara, Shotaro ; Kikuta, Satoshi ; Kobayashi, Masakazu A. R. ; Kusakabe, Haruka ; Lee, Chien-Hsiu ; Liang, Yongming ; Matsuoka, Yoshiki ; Momose, Rieko ; Nagao, Tohru ; Nakajima, Kimihiko ; Tadaki, Ken-ichi. / 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. In: Astrophysical Journal. 2021 ; Vol. 911, No. 2.

Bibtex

@article{8cc8710f7cc84afb9e73cca46ff7c9aa,
title = "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",
abstract = "We present a new catalog of 9318 Ly alpha 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 deg(2)) 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 similar or equal to 80%-100% at bright NB magnitudes of less than or similar to 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.",
author = "Yoshiaki Ono and Ryohei Itoh and Takatoshi Shibuya and Masami Ouchi and Yuichi Harikane and Satoshi Yamanaka and Inoue, {Akio K.} and Toshiyuki Amagasa and Daichi Miura and Maiki Okura and Kazuhiro Shimasaku and Ikuru Iwata and Yoshiaki Taniguchi and Seiji Fujimoto and Masanori Iye and Jaelani, {Anton T.} and Nobunari Kashikawa and Shotaro Kikuchihara and Satoshi Kikuta and Kobayashi, {Masakazu A. R.} and Haruka Kusakabe and Chien-Hsiu Lee and Yongming Liang and Yoshiki Matsuoka and Rieko Momose and Tohru Nagao and Kimihiko Nakajima and Ken-ichi Tadaki",
year = "2021",
month = apr,
day = "9",
doi = "10.3847/1538-4357/abea15",
language = "English",
volume = "911",
journal = "Astrophysical Journal",
issn = "0004-637X",
publisher = "Institute of Physics Publishing, Inc",
number = "2",

}

RIS

TY - JOUR

T1 - SILVERRUSH X

T2 - 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

AU - Ono, Yoshiaki

AU - Itoh, Ryohei

AU - Shibuya, Takatoshi

AU - Ouchi, Masami

AU - Harikane, Yuichi

AU - Yamanaka, Satoshi

AU - Inoue, Akio K.

AU - Amagasa, Toshiyuki

AU - Miura, Daichi

AU - Okura, Maiki

AU - Shimasaku, Kazuhiro

AU - Iwata, Ikuru

AU - Taniguchi, Yoshiaki

AU - Fujimoto, Seiji

AU - Iye, Masanori

AU - Jaelani, Anton T.

AU - Kashikawa, Nobunari

AU - Kikuchihara, Shotaro

AU - Kikuta, Satoshi

AU - Kobayashi, Masakazu A. R.

AU - Kusakabe, Haruka

AU - Lee, Chien-Hsiu

AU - Liang, Yongming

AU - Matsuoka, Yoshiki

AU - Momose, Rieko

AU - Nagao, Tohru

AU - Nakajima, Kimihiko

AU - Tadaki, Ken-ichi

PY - 2021/4/9

Y1 - 2021/4/9

N2 - We present a new catalog of 9318 Ly alpha 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 deg(2)) 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 similar or equal to 80%-100% at bright NB magnitudes of less than or similar to 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.

AB - We present a new catalog of 9318 Ly alpha 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 deg(2)) 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 similar or equal to 80%-100% at bright NB magnitudes of less than or similar to 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.

U2 - 10.3847/1538-4357/abea15

DO - 10.3847/1538-4357/abea15

M3 - Journal article

VL - 911

JO - Astrophysical Journal

JF - Astrophysical Journal

SN - 0004-637X

IS - 2

M1 - 78

ER -

ID: 260543103