标题： 模拟观测平面上的高速云：以史密斯云为例的初步研究 显示英文标题

标题： Simulating High-Velocity Clouds in the Observational Plane: An Initial Study with the Smith Cloud

摘要： 高自转速度云（HVC）可能为银河系未来的恒星形成提供燃料，但它们必须首先在穿越炽热的星系晕时存活下来。 尽管最近的研究提高了我们对云风相互作用生存条件的理解，但很少有观测对比来验证这一条件。 因此，我们首次比较了史密斯云（SC；$d=$12.4 kpc,$l, b = 40^{\circ}, -13^{\circ}$）在GALFA-HI巡天中映射的结果与模拟结果。 我们利用史密斯云的观测特性，用Enzo-E（一种磁流体力学代码）在风洞模拟中模拟了类似的云。 对于观测和模拟，我们都生成了矩量图，通过投影的一阶速度结构函数（VSF）表征湍流，并用归一化的自协方差函数对HI列密度进行相同处理。 我们探索了初始云条件（如半径、金属丰度、热压强、观测角度和距离）如何影响这些统计量，证明小尺度VSF对云湍流敏感，而大尺度则依赖于云的整体速度和观测角度。 我们发现一些模拟重现了关键的观测特征（特别是列密度与速度弥散之间的相关性），但没有一个模拟同时匹配所有观测探针（列密度自协方差的大尺度尤其具有挑战性）。 我们发现显示出通过湍动辐射混合层（TRML）增长的模拟云（云C）是最优匹配，这表明TRML介导冷却对银河系HVC的重要性。 最后，我们建议改进模拟以更好地匹配观测到的HVC。 显示英文摘要

摘要： High-velocity clouds (HVCs) may fuel future star formation in the Milky Way, but they must first survive their passage through the hot halo. While recent work has improved our understanding of the survival criterion for cloud-wind interactions, few observational comparisons exist that test this criterion. We therefore present an initial comparison of simulations with the Smith Cloud (SC; $d=$ 12.4 kpc, $l, b = 40^{\circ}, -13^{\circ}$) as mapped with the GALFA-HI survey. We use the Smith Cloud's observed properties to motivate simulations of comparable clouds in wind tunnel simulations with Enzo-E, an MHD code. For both observations and simulations, we generate moment maps, characterize turbulence through a projected first-order velocity structure function (VSF), and do the same for HI column density with a normalized autocovariance function. We explore how initial cloud conditions (such as radius, metallicity, thermal pressure, viewing angle, and distance) affect these statistics, demonstrating that the small-scale VSF is sensitive to cloud turbulence while large scales depend on cloud bulk velocity and viewing angle. We find that some simulations reproduce key observational features (particularly the correlation between column density and velocity dispersion) but none match all observational probes at the same time (the large scales of the column density autocovariance is particularly challenging). We find that the simulated cloud (cloud C) showing growth via a turbulent radiative mixing layer (TRML) is the best match, implying the importance of TRML-mediated cooling for Milky Way HVCs. We conclude by suggesting improvements for simulations to better match observed HVCs.