Abstract:
   As cradles of stars and building blocks of galaxies, open clusters  encode valuable information about star formation and galaxy evolution.  Precise measurement of open cluster parameters faces challenges from  severe field contamination and unresolved binaries. In this talk, I will  introduce MiMO (Mixture model for open clusters), a Bayesian mixture  model framework that achieves self-consistent inference of cluster  membership and physical parameters . Applying MiMO to Gaia DR3, we  propose a quantitative validation method using Bayesian Evidence (model  comparison) to rigorously distinguish genuine clusters from random field  fluctuations . We further present the MiMO catalog of 1,232 clusters,  providing the first large-scale homogeneous dataset of Present-Day Mass  Functions. Finally, we use this catalog to confirm a Universal Initial  Mass Function and observationally disentangle the timescales of mass  segregation and evaporation (~400 Myr) .