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Machine learning offers a powerful way to analyze large astronomical datasets and identify stars with unusual properties. In this study, students used Extreme Gradient Boosting and Isolation Forest models to investigate why some red clump stars deviate from their expected luminosities. Their results show that stellar age plays a much stronger role than metallicity in driving luminosity variation, and the models successfully detected outlier stars that may affect distance measurements in galactic studies.

In this article, the authors systematically study whether the type of a star is correlated with the number of planets it can support. Their study shows that medium-sized stars are likely to support more than one planet, just like the case in our solar system. They predict that, of the hundreds of planets beyond our solar system, 6% might be habitable. As humans work to travel further and further into space, some of those might truly be suited for human life.