Project B
Dense, star-forming gas — the missing piece in understanding the lifecycle of molecular clouds
Lukas Neumann (ESO), Ashley Barnes (ESO)
Giant molecular clouds are the birthplaces of stars. In the densest parts of molecular clouds stars form as a consequence of gravitational collapse, forming stellar clusters, which, in turn, feed back energy into the surrounding medium hence dispersing the cloud. This sequence of turbulent processes results in a lifecycle of molecular clouds, which controls how galaxies form and evolve. However, there is very little known about the densest, immediate star-forming parts of giant molecular clouds since observing this dense gas phase at scales of individual clouds in external galaxies is challenging and expensive, even with the most advanced radio telescopes, such as ALMA.
In this project, we will attempt the first steps towards understanding how efficiently dense gas forms from bulk molecular gas, how fast dense gas is subsequently converted into stars and how effective stellar feedback is in dispersing dense gas. This novel approach will put the first constraints on these dense gas efficiencies and timescales — the missing piece in understanding the cloud lifecycle.
We will undergo this task by using recent, high-resolution, cloud-scale ALMA observations of dense gas tracers across a nearby spiral galaxy called NGC 2903. These tracers rely on molecular line emission from molecules that only emit at higher gas densities, such as HCN or HCO+. Paired with cloud-scale observations of the bulk molecular gas from ALMA and tracers of active star-forming regions from VLT (MUSE) and JWST, we will use the spatial offset between gas and stars only seen at high-resolution to model the various phases of the the lifecycle of molecular clouds.
During this project, the student will work with multi-wavelength data of a nearby galaxy from radio (ALMA) over optical (VLT) to infrared (JWST) observations. There will be an emphasis on reducing and processing molecular line emission from ALMA. The student will image ALMA data cubes with the Common Astronomical Software Applications (CASA) software, visualise the data with the Cube Analysis and Rendering Tool for Astronomy (CARTA), and process the data with Python. This project focuses on working with observational data of nearby, star-forming galaxies but also employs a theoretical model of the lifecycle of molecular clouds that is fitted to the data.