The observational signatures of molecular cloud formation

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.