Abstract:Identifying and characterizing young populations of star-forming regions is crucial to unravel their properties. In this regard, Gaia-DR3 data and machine learning tools are very useful for studying large star-forming complexes. In this work, we analyze the ∼ 7.1degree 2 area of one of our Galaxy's dominant feedback-driven star-forming complexes, i.e., the region around Trumpler 37. Using the Gaussian mixture and random forest classifier methods, we identify 1243 high-probable members in the complex, of which … Show more
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