Discovering and Characterizing Embedded Stellar Clusters in the Near-Infrared

Abstract

I present a near-infrared search for new embedded stellar clusters in the Galaxy, and the results of near-infrared followup observations of a subset of newly discovered stellar clusters. I discuss the initial mass function of these embedded clusters and the implications of the apparent method-dependent systematic error in the IMF.First, I present near-infrared J, H, and K images of six embedded stellar clusters in the Galaxy, and K-band spectroscopy for two. I find a significant fraction of pre-main-sequence stars present in at least two of the clusters. For the clusters dominated by main-sequence stars, we determine the initial mass function (IMF) both by using the K luminosity function and a global extinction correction and by deriving individual extinction corrections for each star based on their placement in the K vs. H-K color-magnitude diagram. Based on our IMFs we find a significant discrepancy between the mean IMF derived via the different methods, suggesting that taking individual extinctions into account is necessary to correctly derive the IMF for an embedded cluster. I find that using the KLF alone to derive an IMF is likely to produce an overly steep slope in stellar clusters subject to variable extinction, and examine literature results to see if the same effect exists in the work of other authors.I conduct a two-phase search of the 2MASS Point Source Catalog to discover previously unknown embedded stellar clusters and construct a more complete sample than has previously been available. Based on comparisons with the sample of known embedded stellar clusters we determine the completeness of the total existing sample to be ~75% within 2 kpc. I discuss the limitations of previously employed algorithms for stellar cluster detection, and suggest possible alternatives for use in areas of high stellar background density.Finally I present a detailed look at two of the incorrectly identified embedded cluster candidates to better understand the limitations of our algorithm.