表征条件星系属性分布：基于高斯混合模型
Characterizing the Conditional Galaxy Property Distribution using Gaussian Mixture Models
AI总结：
本文提出用高斯混合模型表征条件星系属性分布（CGPD），基于半解析模型和IllustrisTNG模拟验证了约3个高斯分量即可高精度描述，为线强度映射提供建模框架。
摘要：
线强度映射（LIM）是一种约束星系属性全局分布的有前景的技术。为了将探测不同示踪物的LIM实验与传统的星系巡天相结合，并充分利用这些观测的科学潜力，有必要建立一个具有物理动机的建模框架。作为发展这样一个框架的一部分，在本工作中，我们引入并模拟了条件星系属性分布（CGPD），即星系属性在宿主暗物质晕质量和红移条件下的分布。我们考虑了五个星系属性，包括星系恒星质量、分子气体质量、星系半径、气相金属丰度和恒星形成率（SFR），这些属性对于预测感兴趣的发光线至关重要。CGPD代表了星系在五维属性空间中的完整分布；许多重要的星系分布函数和标度关系，如恒星质量函数和SFR主序，都可以通过对其积分和投影导出。我们利用两种不同类型的宇宙学星系模拟，一个半解析模型和IllustrisTNG流体动力学模拟，来表征CGPD并探索使用高斯混合模型（GMM）表示它的效果。我们发现，仅需少数（约3个）高斯分量，GMM就能以高精度描述两种模拟中星系的CGPD。通过构建星系属性与相关可观测示踪物之间的适当关系，CGPD可以被映射到LIM或其他可观测量的预测上。
英文摘要：
Line-intensity mapping (LIM) is a promising technique to constrain the global distribution of galaxy properties. To combine LIM experiments probing different tracers with traditional galaxy surveys and fully exploit the scientific potential of these observations, it is necessary to have a physically motivated modeling framework. As part of developing such a framework, in this work we introduce and model the conditional galaxy property distribution (CGPD), i.e. the distribution of galaxy properties conditioned on the host halo mass and redshift. We consider five galaxy properties, including the galaxy stellar mass, molecular gas mass, galaxy radius, gas phase metallicity and star formation rate (SFR), which are important for predicting the emission lines of interest. The CGPD represents the full distribution of galaxies in the five dimensional property space; many important galaxy distribution functions and scaling relations, such as the stellar mass function and SFR main sequence, can be derived from integrating and projecting it. We utilize two different kinds of cosmological galaxy simulations, a semi-analytic model and the IllustrisTNG hydrodynamic simulation, to characterize the CGPD and explore how well it can be represented using a Gaussian mixture model (GMM). We find that with just a few ($\sim 3$) Gaussian components, a GMM can describe the CGPD of the simulated galaxies to high accuracy for both simulations. The CGPD can be mapped to LIM or other observables by constructing the appropriate relationship between galaxy properties and the relevant observable tracers.
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