星系团弛豫的光学测光指标
Optical Photometric Indicators of Galaxy Cluster Relaxation
AI总结：
本研究评估基于redMaPPer算法的简单光学测光指标识别弛豫星系团的能力，发现结合BCG对比指标与少量ICM信息可实现约60%的纯度，同时指出BCG识别是当前的核心挑战。
摘要：
动力学弛豫程度最高的星系团在宇宙学研究以及星系团内介质（ICM）、活动星系核反馈的天体物理研究中发挥着特殊作用。长期以来，对ICM形态的高空间分辨率成像是确定星系团动力学状态的金标准，但当前或规划中的巡天项目无法提供这类数据，因此需要单独的定向后续观测。鉴于光学和/或近红外测光成像，以及基于这些数据得到的红序星系团搜寻结果，预计将广泛覆盖即将开展的光学和毫米波巡天发现的星系团，有必要探究仅靠测光数据能在多大程度上识别弛豫星系团候选体，再投入资源开展如高分辨率X射线观测等后续研究。本研究基于在斯隆数字巡天（Sloan Digital Sky Survey）数据上运行的redMaPPer星系团搜寻算法，评估了几种简单测光测量方法对X射线选大质量星系团样本的动力学状态X射线分类的复现能力。研究发现，两个对比亮中心星系（BCG）与其他星系团成员的简单指标，可在我们的数据集中识别出纯度约40%的完整弛豫星系团样本。加入以中心位置形式呈现的少量ICM信息后，纯度提升至约60%。但所有三个指标都严重依赖对BCG的正确识别，这目前仍是光学红序星系团搜寻算法面临的挑战。
英文摘要：
The most dynamically relaxed clusters of galaxies play a special role in cosmological studies as well as astrophysical studies of the intracluster medium (ICM) and active galactic nucleus feedback. While high spatial resolution imaging of the morphology of the ICM has long been the gold standard for establishing a cluster's dynamical state, such data are not available from current or planned surveys, and thus require separate, pointed follow-up observations. With optical and/or near-IR photometric imaging, and red-sequence cluster finding results from those data, expected to be ubiquitously available for clusters discovered in upcoming optical and mm-wavelength surveys, it is worth asking how effectively photometric data alone can identify relaxed cluster candidates, before investing in, e.g., high-resolution X-ray observations. Here we assess the ability of several simple photometric measurements, based on the redMaPPer cluster finder run on Sloan Digital Sky Survey data, to reproduce X-ray classifications of dynamical state for an X-ray selected sample of massive clusters. We find that two simple metrics contrasting the Bright Central Galaxy (BCG) to other cluster members can identify a complete sample of relaxed clusters with a purity of ~40 per cent in our data set. Including minimal ICM information in the form of a center position increases the purity to ~60 per cent. However, all three metrics depend critically on correctly identifying the BCG, which is presently a challenge for optical red-sequence cluster finders.
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