发表机构
National Cheng Kung University; MIT Kavli Institute, Massachusetts Institute of Technology; Department of Physics, Massachusetts Institute of Technology; Center for Frontier Science, Chiba University; Department of Physics, Graduate School of Science, Chiba University; RIKEN Center for Advanced Intelligence Project; Subaru Telescope, National Astronomical Observatory of Japan; The Inter-University Centre for Astronomy and Astrophysics; Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo（国立成功大学; 麻省理工学院卡弗里宇宙学研究所; 麻省理工学院物理系; 千叶大学前沿科学中心; 千叶大学理学研究科物理学系; 理化学研究所先进智能项目中心; 昴星望远镜，日本国立天文台; 大学间天体物理研究中心; 东京大学宇宙物质量子物理卡弗里研究所（WPI），东京大学高等研究院（UTIAS））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发fCAMIRA工具对HSC-SSP Y3的129个弱引力透镜剪切选择星系团进行光学证认，构建高精度测光红移样本，约8%样本因投影效应存在红移差异。
AI 中文摘要
我们开发了用于光学星系团证认的工具fCAMIRA（强制模式CAMIRA），并将其应用于从Hyper Suprime-Cam Subaru战略项目三年期（HSC-SSP Y3）弱引力透镜数据得到的孔径质量图中识别出的129个弱引力透镜（WL）剪切选择星系团样本。fCAMIRA基于CAMIRA星系团搜寻算法构建，依赖于以数据驱动方式校准的红序（RS）星系模型。该RS模型采用本研究利用X射线选择星系团测量得到的金属度-光度关系，其红移范围达z≈1.3，随后利用大型光谱样本校准颜色偏移。借助该RS模型，我们构建了两类星系丰度图：一类采用与典型星系团大小R=0.8 h⁻¹ Mpc匹配的空间滤波器，另一类采用与构建WL孔径质量图所用相同的固定角尺寸滤波器。fCAMIRA算法利用这两类丰度图，识别每个剪切选择星系团视线上的所有光学对应体候选，并根据排名最高的对应体测量星系团的测光红移，排名由每个候选的引力透镜贡献分数f_lens决定。利用已有的星系团光谱红移，我们量化得到星系团测光红移的平均偏差约为0.005，散度约为0.008，展现出优异的测光红移性能。我们将fCAMIRA测光红移与直接位置交叉匹配的估计值进行比较，发现总样本中约8%的星系团存在大于0.15的红移差异，这一异常值比例主要归因于投影效应，导致光学对应体的误认。
英文摘要
We develop fCAMIRA (forced-mode CAMIRA), a tool for optical cluster confirmation, and apply it to a sample of 129 weak-lensing (WL) shear-selected galaxy clusters identified in aperture-mass maps obtained from the Hyper Suprime-Cam Subaru Strategic Program Three-Year (HSC-SSP Y3) weak-lensing data. fCAMIRA is built upon the CAMIRA cluster-finding algorithm and relies on a red-sequence (RS) galaxy model that is calibrated in a data-driven way. The RS model adopts the metallicity-luminosity relation measured in this work using X-ray-selected clusters up to redshift $z\approx1.3$, followed by the calibration of color offsets using large spectroscopic samples. With the RS model, we build two types of galaxy richness maps, one obtained with a spatial filter matched to a typical cluster size of $R=0.8\,h^{-1}\,\mathrm{Mpc}$ and the other obtained with a fixed angular-size filter identical to that used in constructing the WL aperture-mass maps. The fCAMIRA algorithm utilizes these two richness maps, identifies all optical counterpart candidates along the line of sight of each shear-selected cluster, and measures the cluster photometric redshift from the highest-ranked counterpart. The ranking is determined by the fractional lensing contribution $f_{\mathrm{lens}}$ of each candidate. Using available spectroscopic cluster redshifts, we quantify the mean bias and scatter in the cluster photometric redshifts at levels of approximately 0.005 and 0.008, respectively, demonstrating excellent photometric-redshift performance. We compare the fCAMIRA photometric redshifts with estimates from direct positional cross-matching and find that approximately 8% of the total sample exhibits redshift discrepancies greater than 0.15. This outlier fraction is primarily attributed to projection effects, leading to the misidentification of the optical counterparts. (abridged)
Comments26 pages, 16 figures. The catalog is available via https://github.com/inonchiu/hsc_shear_selected_clusters. Published in the Open Journal of Astrophysics
