Constraining the Complex Relationship Between Galaxies and their Dark Matter Haloes with Machine Learning

通过机器学习约束星系及其暗物质晕之间的复杂关系

• 批准号: 2755550
• 金额: --
• 依托单位: Liverpool John Moores University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2755550
• 关键词: Constraining; Complex; Relationship; Between; Galaxies

The project will utilize a class of machine learning algorithm known as sparse regression methods (SRM) to extract robust relationships between the diverse properties of galaxies, their gaseous environments and their host dark matter haloes, formed in cosmological hydrodynamical simulations of the galaxy population. A vast quantity of galaxy parameter relationships may have the potential to be predicted by the simulations (such as formation time, spin, merger history), but are difficult to quantify through direct means. This is where the benefit of SRM is presented clearly, as it is a method that (unlike other machine learning techniques) discards unneeded free parameters and efficiently extracts the "governing" equations of physical systems from state descriptions of the system alone, without a need for detailed prior understanding of the relevant physics. This would also allow for galaxy populations to be "painted" onto dark matter-only simulations, which are relatively inexpensive to generate compared to full baryonic simulations through a process known as halo modelling.

该项目将利用一类称为稀疏回归方法（SRM）的机器学习算法来提取星系的不同属性、其气体环境及其宿主暗物质晕之间的稳健关系，这些关系是在星系群的宇宙学流体动力学模拟中形成的。大量的星系参数关系可能有可能通过模拟来预测（例如形成时间、自转、合并历史），但很难通过直接手段量化。这就是 SRM 的好处显而易见的地方，因为它是一种（与其他机器学习技术不同）丢弃不需要的自由参数并仅从系统的状态描述中有效提取物理系统的“控制”方程的方法，而不需要详细了解相关物理知识。这还允许将星系群“绘制”到仅暗物质的模拟上，与通过称为光晕建模的过程进行的完整重子模拟相比，这种模拟的生成成本相对较低。