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Candidate star clusters toward the inner Milky Way discovered on deep-stacked KS-band images from the VVV Survey

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The census of star clusters in the inner Milky Way is incomplete because of extinction and crowding. We embarked on a program to expand the star cluster list in the… Click to show full abstract

The census of star clusters in the inner Milky Way is incomplete because of extinction and crowding. We embarked on a program to expand the star cluster list in the direction of the inner Milky Way using deep stacks of Ks-band images from the VISTA Variables in Via Lactea (VVV) Survey. We applied an automated two-step procedure to the point-source catalog derived from the deep Ks images: first, we identified overdensities of stars, and then we selected only candidate clusters with probable member stars that match an isochrone with a certain age, distance, and extinction on the color-magnitude diagram. This pilot project only investigates the cluster population in part of one VVV tile, that is, b201. We identified nine cluster candidates and estimated their parameters. The new candidates are compact with a typical radius on the sky of ~0.2--0.4 arcmin (~0.4-1.6 pc at their estimated distances). They are located at distances of ~5-14 kpc from the Sun and are subject to moderate extinction of E(B-V)=0.4-1.0 mag. They are sparse, probably evolved, with typical ages log(t/1 yr)~9. Based on the locations of the objects inside the Milky Way, we conclude that one of these objects is probably associated with the disk or halo and the remaining objects are associated with the bulge or the halo. The cluster candidates reported here push the VVV Survey cluster detection to the limit. These new objects demonstrate that the VVV survey has the potential to identify thousands of additional cluster candidates. The sub-arcsec angular resolution nd the near-infrared wavelength regimen give it a critical advantage over other surveys.

Keywords: inner milky; vvv survey; milky way; star

Journal Title: Astronomy and Astrophysics
Year Published: 2017

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