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Simulating Population III star formation

模拟星族 III 恒星的形成

基本信息

批准号：
2268675
负责人：
金额：
--
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2268675
关键词：
Simulating Population III star formation

项目摘要

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.

超音速湍流运动被认为在星星形成过程中起着重要作用。理论表明，“湍流”是塑造星际介质（ISM），调节星星形成，甚至设置恒星的质量，因为它们形成在年轻的星团。然而，ISM中湍流运动的性质很难确定：我们只能使用分子发射线的多普勒频移来探测它们的速度-给我们1D，而不是3D信息-并且这些线通常是光学粗的，因此它们的形状很难解释。湍流ISM的数值模拟可以在这里提供帮助。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，并使用这些来研究湍流的性质。由于我们在3D模拟中知道湍流的“真实”基本属性，因此我们可以测试用于观测数据的标准技术如何在合成观测中执行。我们还可以利用机器学习方法来开发新技术，用于在我们的模拟和观测数据集中研究ISM湍流的特性。在这个博士学位的工作将向您介绍广泛的ISM物理-如ISM化学，分子线辐射传输和流体动力学-以及先进的统计和机器学习技术。