Regression on Planetary Mass in Protoplanetary Disks


The mass of planets forming within protoplanetary disks is a fundamental parameter that influences their evolution, atmospheric composition, and potential for hosting life. This project proposes to develop a machine learning model that can accurately regress on the mass of planets observed in protoplanetary disks. Utilizing observational data, including disk morphology, spectral signatures, and dynamical interactions within the disk, the model will aim to predict the mass of forming planets. This approach addresses the challenge of directly measuring planetary masses in these environments, offering a novel tool for astronomers to infer planet formation processes and disk-planet interactions.


Total project length: 175/350 hours.

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