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
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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DOI：
10.3847/1538-4357/abea15
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Y. Ono;R. Itoh;T. Shibuya;M. Ouchi;Y. Harikane;Satoshi Yamanaka;A. Inoue;Toshiyuki Amagasa;Daichi Miura;Maiki Okura;K. Shimasaku;I. Iwata;Y. Taniguchi;S. Fujimoto;M. Iye;A. Jaelani;N. Kashikawa;Shotaro Kikuchihara;S. Kikuta;Masakazu A. R. Kobayashi;H. Kusakabe;Chien-Hsiu Lee;Yongming Liang;Y. Matsuoka;R. Momose;T. Nagao;K. Nakajima;K. Tadaki
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文献类型：
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作者：
Y. Ono;R. Itoh;T. Shibuya;M. Ouchi;Y. Harikane;Satoshi Yamanaka;A. Inoue;Toshiyuki Amagasa;Daichi Miura;Maiki Okura;K. Shimasaku;I. Iwata;Y. Taniguchi;S. Fujimoto;M. Iye;A. Jaelani;N. Kashikawa;Shotaro Kikuchihara;S. Kikuta;Masakazu A. R. Kobayashi;H. Kusakabe;Chien-Hsiu Lee;Yongming Liang;Y. Matsuoka;R. Momose;T. Nagao;K. Nakajima;K. Tadaki
我们提出了一个新的9318 Lyα发射体（LAE）候选者的目录，z = 2.2，3.3，4.9，5.7，6.6，和7.0，由SILVERRUSH程序使用机器学习技术从大面积上进行光度选择（高达25.0 deg 2）的成像数据与六个窄带过滤器采取的斯巴鲁战略计划与超级超级凸轮和斯巴鲁密集计划，宇宙氢再电离与斯巴鲁揭开面纱。我们构建了一个卷积神经网络，区分真实的LAE和污染物之间的完整性为94%，污染率为1%，使我们能够有效地去除污染物从光度选择的LAE候选人。我们确认我们的LAE目录包括177个LAE，这些LAE在我们的SILVERRUSH计划和以前的研究中已经被光谱识别，确保了我们的机器学习选择的有效性。此外，我们发现，在明亮的NB星等为1.24等时，我们的LAE目录与我们之前的结果之间的对象匹配率为0.80%-100%。我们还确认我们的LAE候选者的表面数密度与之前的结果一致。我们的LAE目录将在我们的项目网页上公开。
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.