旋转日冕模型
Models of rotating coronae
摘要：
将平衡动力学模型与观测数据相匹配是理解围绕我们和其他星系的气态热晕结构的重要一步。然而，文献中使用的两种主要类型的模型不适合这项任务：（i）简单的正压模式是解析的，因此可以调整以符合观测结果，但显然是不现实的，因为旋转速度$v_ϕ(R,z)$不取决于到银道面的距离$z$，而（ii）作为宇宙学星系形成模拟的结果而获得的模型更真实，但由于计算成本高而不切实际地拟合观测。在这里，我们提出了一个通用的方法来构建轴对称斜压平衡模型的旋转星系冕在任意外部潜力的差距。我们特别考虑一类模型，其在$(R,z)$平面上的等压面是变轴比的椭圆。这些模型是由两个一维函数定义的，轴向压力比$q_{\rm axis}(z)$和沿星系对称轴的压力值$P_{\rm axis}(z)$。这些模型可以有一个旋转速度$v_ϕ(R,z)$，实际上随着远离银道面而降低，并且可以重现宇宙学模拟中发现的角动量分布。该模型的计算成本低，因此可以用于拟合算法。我们提供了一个python代码，给定$q_{\rm axis}(z)$，$P_{\rm axis}(z)$和$Φ(R,z)$返回$ρ(R,z)$，$T(R,z)$，$P(R,z)$，$v_ϕ(R,z)$。我们展示了一些例子，这些模型使用银河系作为案例研究。
英文摘要：
Fitting equilibrium dynamical models to observational data is an essential step in understanding the structure of the gaseous hot haloes that surround our own and other galaxies. However, the two main categories of models that are used in the literature are poorly suited for this task: (i) simple barotropic models are analytic and can therefore be adjusted to match the observations, but are clearly unrealistic because the rotational velocity $v_ϕ(R,z)$ does not depend on the distance $z$ from the galactic plane, while (ii) models obtained as a result of cosmological galaxy formation simulations are more realistic, but are impractical to fit to observations due to high computational cost. Here we bridge this gap by presenting a general method to construct axisymmetric baroclinic equilibrium models of rotating galactic coronae in arbitrary external potentials. We consider in particular a family of models whose equipressure surfaces in the $(R,z)$ plane are ellipses of varying axis ratio. These models are defined by two one-dimensional functions, the axial ratio of pressure $q_{\rm axis}(z)$ and the value of the pressure $P_{\rm axis}(z)$ along the galaxy's symmetry axis. These models can have a rotation speed $v_ϕ(R,z)$ that realistically decreases as one moves away from the galactic plane, and can reproduce the angular momentum distribution found in cosmological simulations. The models are computationally cheap to construct and can thus be used in fitting algorithms. We provide a python code that given $q_{\rm axis}(z)$, $P_{\rm axis}(z)$ and $Φ(R,z)$ returns $ρ(R,z)$, $T(R,z)$, $P(R,z)$, $v_ϕ(R,z)$. We show a few examples of these models using the Milky Way as a case study.
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