Membership Analysis and 3D Kinematics of the Star-forming Complex around Trumpler 37 Using Gaia-DR3

Abstract Identifying and characterizing young populations of star-forming regions are crucial to unraveling their properties. In this regard, Gaia-DR3 data machine-learning tools very useful for studying large complexes. work, we analyze the ∼7.1 deg 2 area one our Galaxy’s dominant feedback-driven complexes, i.e., region around Trumpler 37. Using Gaussian mixture random-forest classifier methods, identify 1243 highly probable members in complex, which ∼60% new complete down mass limit ∼0.1–0.2 M ⊙ . The spatial distribution stars reveals multiple clusters toward where central cluster massive star HD 206267 two subclusters. Of stars, 152 have radial velocity, with a mean value −16.41 ± 0.72 km s −1 We investigate stars’ internal relative movement within cluster. kinematic analysis shows that cluster’s expansion is relatively slow compared whole complex. This possibly due newly formed discuss these results context hierarchical collapse feedback-induced mode formation