An objective, automated method to detect and characterize semi-resolved star clusters

ABSTRACT We present a novel method for automatically detecting semiresolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster’s radius, but larger than the separations between individual stars. We apply our method to a 1.77 deg$^2$ field in the Large Magellanic Cloud using the VISTA survey of the Magellanic Clouds (VMC). Our approach first models the position-dependent PSF to remove point sources, leaving extended objects such as star clusters and background galaxies. We then analyse these extended objects’ isophotes to characterize their properties, perform integrated photometry, and remove spurious objects. We demonstrate our approach on a deep VMC $K_\text{s}$ tile containing the most actively star-forming regions in the LMC: 30 Doradus, N158, N159, and N160. This tile is challenging for automated techniques owing to crowding and nebular emission. We detect 640 candidate star clusters, estimating 20 per cent contamination from background galaxies and chance stellar blends. Visual inspection of our 640-candidate automated list yields 480 likely clusters. Using the Magellanic Mopra Assessment survey, we find 140 of the likely clusters are embedded. We compare with James Webb Space Telescope data that overlaps with 68 of our 640 automated candidates; we find that 80 per cent appear to be star clusters. Our method can be applied to the remaining VMC tiles and to space-based observations of galaxies like M31 and M33, and our PSF-fitting approach enables PSF characterization and removal in other wide-field imaging surveys.