CDS&E: Harnessing Self-Organizing Maps for the Discovery of Star Formation in Molecular Clouds

University Of Texas At Austin, Austin TX

Investigators

Abstract

Stars like our Sun are born in large clouds of dust and gas that make thousands of stars at once. Astronomers observe these gas clouds to study young stars and learn how stars and planets form. These clouds are complex, and the observations are difficult to interpret. This project uses machine learning to address an important mystery in astronomy: Why do stars have a great variety of total mass? Stars are found to have a mass between a tenth the mass of our Sun and up to 100 times the mass of the Sun. The Investigators will use computer models of forming stars to test their method and will apply it to recent observations of star-forming regions. The team will develop presentations for the public and train undergraduates in the Texas Astronomy Undergraduate Research experience for Under-represented Students (TAURUS) summer program in machine learning methods. The investigators plan to combine state-of-the-art numerical simulations, unsupervised machine learning, and molecular line observations to study the gas engaged in the earliest stages of star formation within molecular clouds. The team will apply an unsupervised neural network algorithm, known as a Self-Organizing Map (SOM), to identify clusters comprising star-forming structures and the gas that forms them in observational data. As gas structures within molecular clouds are complex and do not have well-defined boundaries, this necessitates new techniques for SOM cluster inference. Molecular line “synthetic observations” will be produced and compared to the observational data to aid in interpretation and exaction of physical properties. This work will build bridges between astronomy, statistics and computer science and thereby advance new approaches for data segmentation and clustering that will be of broader use to the astronomy community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.