Simulating Population III star formation

超音速湍流运动被认为在恒星形成过程中起着重要作用。理论表明，“湍流”负责塑造星际介质(ISM)，调节恒星的形成，甚至在年轻星团中形成恒星时设定它们的质量。然而，ISM中湍流运动的性质很难确定：我们只能利用分子发射线的多普勒频移来探测它们的速度--给我们一维信息，而不是3D信息--而且这些线通常光学上很厚，以至于它们的形状很难解释。对湍流ISM的数值模拟在这一点上可以有所帮助。通过使用跟踪CO和其他物种的形成和破坏的模拟，我们可以对模拟进行综合观察，并使用这些模拟来研究湍流的特性。因为我们在3D模拟中知道湍流的“真实”潜在属性，所以我们可以测试在观测数据上使用的标准技术在合成观测上的表现。我们还可以利用机器学习的方法来开发新的技术，在我们的模拟和观测数据集中研究ISM湍流的特性。这一博士学位的工作将向你介绍广泛的ISM物理--如ISM化学、分子线辐射转移和流体力学--以及高级统计学和机器学习技术。

Supersonic turbulent motions are thought to play a major role in star formation process. Theories have suggested that 'turbulence' is responsible for shaping the interstellar medium (ISM), regulating star formation, and even setting the masses of stars as they form in young clusters. However, the properties of the turbulent motions in the ISM are difficult to determine: we can only use the doppler shifting of molecular emission lines to probe their velocities - giving us 1D, not 3D information - and these lines are often optically thick, such that their shapes are difficult to interpret. Numerical simulations of the turbulent ISM can help here. By using simulations that track the formation and destruction of CO and other species, we can then make synthetic observations of the simulations and use these to study the properties of the turbulence. Since we know the 'true' underlying properties of the turbulence in our 3D simulations, we can then test how the standard techniques used on observational data perform on synthetic observations. We can also make use of a machine learning approach to develop new techniques for studying the properties of ISM turbulence in our simulations and observational datasets. The work in this PhD will introduce you to a wide range of ISM physics - such as ISM chemistry, molecular line radiative transfer, and fluid dynamics - and also advanced statistics and machine learning techniques.