质量吸积历史在星系团形态上的印记
Imprints of Mass Accretion History on Galaxy Cluster Morphology
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中文总结 AI 辅助
本研究基于The300项目的305个大质量星系团，利用MultiCAM方法将星系团形态参数与质量吸积历史关联，实现对星系团MAH的预测，为约束星系团MAH提供了新途径。
中文摘要 AI 辅助
星系团动力学状态的变化会在可观测-质量关系中引入偏差和弥散。星系团的动力学状态是其质量吸积历史（Mass Accretion History, MAH）的涌现特征，因此约束星系团的MAH具有重要意义。本研究基于The300项目中的305个大质量星系团，将其投影恒星分布的特征与MAH相关联，开展相关分析。作为基准，我们首先通过斯皮尔曼秩相关系数ρ_sp，将红移z=0处的宿主暗物质晕动力学状态指标与其MAH相关联，结果显示，次结构质量分数和质心偏移量的测量值与0.1≲z≲1范围内测得的MAH存在强相关性。我们对投影恒星密度图的形态测量重复了上述分析，发现其中许多测量值与MAH的不同时期呈现中等强度的相关性：总体而言，核心形态测量值（r≤30 kpc）与早期MAH的相关性更好，扣除核心区域（50 kpc≤r≤1 Mpc）的形态测量值则与晚期MAH的相关性更好。我们进一步使用多变量条件丰度匹配（Multivariable Conditional Abundance Matching, MultiCAM）量化传统动力学状态指标和形态参数的MAH预测能力，MultiCAM采用简单的秩排序操作，可直接应用于观测数据集。我们发现，对于1≲z≲0.1范围内质量分数的预测，其表现合理（ρ_sp≥0.6），不过使用投影量时会出现显著的信息损失。作为本方法的一个应用实例，我们利用MultiCAM模型的系数选择子样本星系团，这些星系团在给定时间范围内吸积了更多（或更少）其z=0时刻的质量预算。
英文摘要
Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project by connecting features from their projected stellar distributions to their mass accretion histories (MAH). As a baseline, we first correlate host dark matter halo dynamical state indicators at $z=0$ with their MAH via the Spearman rank correlation coefficient $ρ_{\mathrm{sp}}$. Both substructure mass fraction and center-of-mass offset measurements correlate strongly with the MAH measured between $0.1\lesssim z\lesssim 1$. We repeat this exercise with morphological measurements of projected stellar density maps, many of which exhibit moderate correlation strength with different times in the MAH. Broadly, core morphological measurements ($r \leq 30\,\mathrm{kpc}$) correlate better with early-time MAH. Core-excised ($50\,\mathrm{kpc} \leq r \leq 1\,\mathrm{Mpc}$) morphological measurements correlate better with late-time MAH. We further quantify the MAH prediction power of both traditional dynamical state indicators and morphological parameters using Multivariable Conditional Abundance Matching (MultiCAM). MultiCAM employs simple rank-ordering operations, making it straightforward to translate to observed datasets. We find reasonable ($ρ_{\mathrm{sp}} \geq 0.6$) performance for predictions of the mass fraction between $1\lesssim z\lesssim 0.1$, though with notable information loss when using projected quantities. In one example application of our methodology, we use the coefficients of the MultiCAM models to select subsamples of galaxy clusters that have accreted more (or less) of their $z = 0$ mass budget over a given time frame.
