ALMAGAL: a large statistical study of high-mass stellar cluster forming regions

• Applicant: Professor Peter Schilke, Ph.D.
• Subject Area: Astrophysics and Astronomy
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 444687205

The formation of clusters containing high-mass stars is still not very well understood. The scientific interest is twofold: these clusters are the birth environment of at least half of all the low mass stars in existence, including our Sun. Due to gravitational, mechanical and radiative interactions such an environment will modify the conditions for star and disk formation, and thus of planet formation. Low-mass star formation in these conditions has received little attention so far.The other point of interest is high-mass star formation itself, since high-mass stars have a tremendous influence not only on their immediate surroundings, but dominate the energetics on galactic scales. While high-mass star-formation has received increasing attention in the last decade, there is no systematic study on smaller scales.To investigate both science questions in a statistically significant way we have launched the large ALMA project ALMAGAL, which will observe a complete sample of all high-mass star-forming clusters within 7 kpc, a sample of 1017 objects. I am co-PI of this project. The proposal was approved in August 2019, data acquisition and also delivery has started in October 2019, and completion is foreseen for June 2020. As co-PI and co-lead of the Science Working Group Physics I am in an excellent position to exploit and analyze a large fraction of the data, provided enough scientific resources are available.In this application, I describe a science program that concentrates on the questions of physical processes governing the fragmentation of cluster forming clumps and their evolution of time, and the mass accretion process and its dependence on feedback. The methods that will be employed are analysis of the continuum and to a larger degree on molecular line data. This will determine the physical and chemical conditions both on core and cluster scale, as well as the dynamics. It will lay the groundwork for future follow-up programs investigating disk properties of both high-mass and low-mass disks.Since nothing in size and kind similar to this data set has ever produced, we have to develop many new tools to describe the data rapidly, reliably and in a way that allows a meaningful statistical analysis. Machine learning methods seem very good candidates for this task. We plan to make the tools and methods we develop publicly available.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Alvaro Sanchez-Monge