The observational signatures of molecular cloud formation

恒星形成于遍布星际介质（ISM）的密集分子云中。尽管观测研究已经确定了恒星形成的必要条件，但对于这些条件在银河系中如何、为什么以及在哪里出现，几乎没有什么限制。我们目前对分子云形成过程的理解不足是恒星形成预测理论的主要障碍之一。观测只提供了ISM的一个快照，而正在经历云和恒星形成的区域很难用传统方法挑选出来。正因为如此，我们缺乏可靠的时间线和银河系形成的云的全球普查。该项目将使用各种线示踪剂（例如CO， HI， HCN）对银河系平面进行宽视场调查，以绘制云形成最早阶段的分布。人工智能技术将用于开发分子云的分布、稳定性和内部运动学的整体图像，以及它们与银河系局部环境的关系。通过与数值模拟产生的合成观测结果（由P. Clark提供）的比较，我们将利用机器学习技术开发分子云的进化序列，为当前分子云形成和进化的理论提供潜在的深刻见解。

Stars form in dense molecular clouds throughout the interstellar medium (ISM). Although observational studies have established what the conditions necessary for star formation are, very few constraints exist on how, why, and where those conditions arise in the Galaxy. Our current poor understanding of the molecular cloud formation process is one of the major barriers to a predictive theory of star formation.Observations provide only a snapshot in time of the ISM, and regions undergoing cloud and star formation can be difficult to pick out using traditional methods. Because of this we lack a reliable timeline and global census of the clouds the Galaxy has formed. This project will use wide-field surveys of the Milky Way plane in a variety of line tracers (e.g. CO, HI, HCN) to map the distribution of clouds in the earliest phase of formation. Artificial intelligence techniques will be used to develop a holistic picture of the distribution, stability and internal kinematics of molecular clouds, and their relation to their local environment in the Galaxy. Through the comparison with synthetic observations produced by numerical simulations (provided by P. Clark), we will utilise machine learning techniques to develop an evolutionary sequence of molecular clouds, providing a potentially profound insight into current theories of molecular cloud formation and evolution.