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

该项目将利用一种名为稀疏回归方法(SRM)的机器学习算法来提取星系及其气体环境和其宿主暗物质晕之间的牢固关系，这些关系是在对星系群进行宇宙流体动力学模拟时形成的。大量的星系参数关系可能有可能通过模拟来预测(如形成时间、自转、合并历史)，但很难通过直接手段来量化。这就是SRM的好处被清楚地展示出来的地方，因为它是一种方法(与其他机器学习技术不同)，它丢弃了不需要的自由参数，并仅从系统的状态描述有效地提取了物理系统的“控制”方程，而不需要事先详细地了解相关的物理。这也将允许星系群被绘制到仅限暗物质的模拟上，与通过称为晕建模的全重子模拟相比，产生暗物质模拟的成本相对较低。

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.