Surveys in the next decade will deliver large samples of galaxy clusters that transform our understanding of their formation. Cluster astrophysics and cosmology studies will become systematics limited with samples of this magnitude. With known properties, hydrodynamical simulations of clusters provide a vital resource for investigating potential systematics. However, this is only realized if we compare simulations to observations in the correct way. Here we introduce the mock-X analysis framework, a multiwavelength tool that generates synthetic images from cosmological simulations and derives halo properties via observational methods. We detail our methods for generating optical, Compton-y and X-ray images. Outlining our synthetic X-ray image analysis method, we demonstrate the capabilities of the framework by exploring hydrostatic mass bias for the IllustrisTNG, BAHAMAS, and MACSIS simulations. Using simulation derived profiles we find an approximately constant bias b ≈ 0.13 with cluster mass, independent of hydrodynamical method, or subgrid physics. However, the hydrostatic bias derived from synthetic observations is mass-dependent, increasing to b = 0.3 for the most massive clusters. This result is driven by a single temperature fit to a spectrum produced by gas with a wide temperature distribution in quasi-pressure equilibrium. The spectroscopic temperature and mass estimate are biased low by cooler gas dominating the emission, due to its quadratic density dependence. The bias and the scatter in estimated mass remain independent of the numerical method and subgrid physics. Our results are consistent with current observations and future surveys will contain sufficient samples of massive clusters to confirm the mass dependence of the hydrostatic bias.

中文翻译：

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用 mock-X 表征静水质量偏差

未来十年的调查将提供大量星系团样本，从而改变我们对其形成的理解。星团天体物理学和宇宙学研究将成为受限于这种规模样本的系统学。具有已知特性的集群的流体动力学模拟为研究潜在的系统学提供了重要资源。但是，只有当我们以正确的方式将模拟与观察结果进行比较时，才能实现这一点。在这里，我们介绍了 mock-X 分析框架，这是一种多波长工具，可以从宇宙学模拟中生成合成图像，并通过观察方法得出光晕特性。我们详细介绍了生成光学、康普顿和 X 射线图像的方法。概述我们的合成 X 射线图像分析方法，我们通过探索 IllustrisTNG、BAHAMAS 和 MACSIS 模拟的静水质量偏差来展示该框架的功能。使用模拟派生的剖面，我们发现具有集群质量的近似恒定偏差 b ≈ 0.13，与流体动力学方法或亚网格物理无关。然而，来自合成观测的流体静力偏差是依赖于质量的，对于最大规模的星团，b = 0.3。该结果是由单一温度拟合到准压力平衡中具有宽温度分布的气体产生的光谱所驱动的。由于其二次密度依赖性，光谱温度和质量估计值偏低，因为较冷的气体占主导地位。估计质量的偏差和分散与数值方法和亚网格物理无关。