发表机构
Stanford University; Boston Latin School; Northeastern University（斯坦福大学; 波士顿拉丁学校; 东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用SuperBIT多波段数据评估三种颜色在星系团成员识别中的性能，发现$(b-g)$颜色具有最小离散度和最高纯度，被确定为基于红序识别的最佳选择。
AI 中文摘要
星系团富度图提供了星系团中恒星质量分布的有用示踪，但从多波段测光数据中识别星系团成员可能具有挑战性。在这项工作中，我们研究了超压气球载成像望远镜（SuperBIT）在2023年4月科学飞行期间获得的多波段成像数据。利用SuperBIT星系团样本中经光谱证认的星系团成员，我们评估了SuperBIT三个波段可用的三种颜色（$(u-b)$、$(b-g)$和$(u-g)$）的性能。我们使用光谱证认成员在颜色上的$1\\,\sigma$离散度以及星系团成员识别算法的纯度来量化每种颜色的性能。我们发现$(b-g)$在样本中以及SuperBIT探测的红移范围内始终表现出最小的颜色离散度和最高的纯度。因此，我们将$(b-g)$确定为基于红序的星系团成员识别的首选颜色，为未来利用SuperBIT多波段测光数据分析星系团星系种群提供了基础。
英文摘要
Galaxy richness maps provide a useful tracer of the stellar mass distribution in galaxy clusters, but identifying cluster members from multiband photometric data can be challenging. In this work, we investigate multiband imaging data obtained with the Super-Pressure Balloon-borne Imaging Telescope (SuperBIT) during its science flight in April 2023. Using spectroscopically identified cluster members across the SuperBIT cluster sample, we benchmark the performance of the three colors available from the three SuperBIT bands: $(u-b)$, $(b-g)$, and $(u-g)$. We quantify the performance of each color using the $1\,σ$ scatter of the spectroscopically identified members in color and the purity of our cluster-member identification algorithm. We find that $(b-g)$ consistently exhibits the smallest color scatter and highest purity across the sample and over the redshift range probed by SuperBIT. We therefore identify $(b-g)$ as the preferred color for red-sequence-based cluster-member identification, providing a basis for future analyses of the cluster galaxy population using SuperBIT multiband photometry.
CommentsSubmitted to RNAAS
