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.

我们在z = 2.2、3.3、4.9、5.7、6.6 和 7.0处展示了 9318 个 Ly α发射体（LAE）候选者的新目录，这些候选者是由 SILVERRUSH 程序使用机器学习技术从大面积（高达 25.0 度）光度选择的²) 使用六个窄带滤波器的斯巴鲁战略计划与 Hyper Suprime-Cam 和斯巴鲁密集计划、斯巴鲁推出的宇宙氢再电离获取的成像数据。我们构建了一个卷积神经网络，以 94% 的完整性和 1% 的污染率区分真实的 LAE 和污染物，使我们能够有效地从光度选择的 LAE 候选中去除污染物。我们确认我们的 LAE 目录包括 177 个已在我们的 SILVERRUSH 程序和以前的研究中通过光谱识别的 LAE，确保了我们机器学习选择的有效性。此外，我们发现我们的 LAE 目录与我们之前的结果之间的对象匹配率在 ≲ 24 mag 的明亮 NB 星等下为≃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。