Harnessing Machine Learning to Study the Life Cycle of Stars

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Stars like our Sun are born in large clouds of gas that usually produce thousands of stars at once. As the stars form, they add energy back into their environment ('feedback') and influence the birth gas cloud. This feedback appears as fast-moving gas, which sometimes looks like large bubbles. The data are complex, so identifying feedback is difficult. Typically, astronomers have found the feedback "by eye", which is subjective and time-consuming. A new field of computer science, machine learning, provides an alternative approach to find feedback. In machine learning, computer algorithms are trained to identify features in the same way the human brain recognizes objects - like cats, dogs and cars. The investigator's group will use state-of-the-art models of forming stars to train machine learning algorithms to find feedback. They will apply the algorithms to telescope observations and compare with the feedback sources previously found by humans. The investigator will share the models with the public through the Milky Way project, which is an online astronomy program that trains people to identify feedback in telescope images of gas clouds. The program will also train students in research techniques, including undergraduates from underrepresented groups. The proposal addresses a fundamental star formation question: How much mass and energy is associated with stellar feedback in the interstellar medium? To answer this question, the PI and collaborators will use magnetohydrodynamic simulations of forming stars, for which full feedback information is known, to train machine learning algorithms to identify and quantify feedback. Dust and molecular line 'synthetic observations' will be produced and used together with observational data as a training set. The investigators will compare the machine learning identifications to prior visually identified feedback catalogs, including those from the citizen-science Milky Way Project, create an updated census, and publicly release the algorithm and data to the community. The broader impact objectives are to increase public participation in the Milky Way Project, develop a WorldWide Telescope tour on feedback, and train students, including undergraduates in the Texas Astronomy Undergraduate Research experience for Under-Represented Students (TAURUS) summer program.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.