Student Investigator: Vedant Chandra
Mentor: Kevin Schlaufman (Physics and Astronomy, KSAS)

Metal-poor stars are 10 billion year-old local relics of the early Universe. Therefore, their characteristics can be used to infer the properties of the first stellar generation and the earliest evolution of the Milky Way. These metal-poor stars are, however, rare and hard to find – only a small fraction of the Milky Way’s metal-poor stellar population has been characterized. One significant challenge in this field is the spectroscopic similarity between rare metal-poor main sequence stars and common cool white dwarfs. Our project develops machine learning algorithms for large spectroscopic surveys, tuned to break this degeneracy using Bayesian convolutional neural networks. We will publish our metal-poor star discoveries and distribute our software tools for use by the broader astronomical community.