Stellar clusters provide a unique look into the populations of stars in our galaxy through different slices of time. The evolution of a population of stars is highly subject to initial conditions, and as such, through careful observation and rigorous analysis, one can infer those initial conditions from the comparison to models. In this work, we present our methods and findings on the process of filtering, fitting, and statistical analysis of 17 nearby open clusters. For each cluster, we have confidently identified cluster members before fitting thousands of permutations of synthetic isochrones and color excess to determine the best fit. We present Gaia-based ages, metallicities, membership counts, and interstellar reddening of 17 open clusters. For 6 of these clusters, we are the first to present Gaia-based determinations of the aforementioned properties. Additionally, we analyze the surface density and average mass drop-off of each open cluster. Finally, we delve into the population statistics of open clusters in the Milky Way, showing correlations such as mean cluster mass as a function of age.
Astrophysical Sciences and Technology (MS)
Department, Program, or Center
School of Physics and Astronomy (COS)
Wainwright, William J., "Delving into population statistics of open clusters with Gaia: A Python driven exploration and characterization of open stellar clusters in the Milky Way" (2022). Thesis. Rochester Institute of Technology. Accessed from
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