Consensus-based algorithm for the nonparametric detection of star clusters (CANDiSC)

Abstract

Context. The VISTA Variables in the Vía Láctea (VVV) and its eXtension (VVVX) are near-infrared surveys mapping the Galactic bulge and adjacent disk. These datasets have enabled the discovery of numerous star clusters obscured by high and spatially variable extinction. However, most previous searches relied on visual inspection of individual tiles, which is inefficient and biased against faint or low-density systems. Aims. We aim to develop an automated, homogeneous algorithm for systematic cluster detection across different surveys. Here, we aim to apply our method to VVVX data covering low-latitude regions of the Galactic bulge and disk, affected by extinction and crowding. Methods. We introduce the Consensus-based Algorithm for Nonparametric Detection of Star Clusters (CANDiSC), which integrates kernel-density estimation (KDE), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and nearest-neighbor density estimation (NNDE) within a consensus framework. A stellar overdensity is classified as a candidate if identified by at least two of these methods. We applied CANDiSC to 680 tiles in the VVVX PSF photometric catalogue, covering ≍ 1100, deg2. Results. We detect 163 stellar overdensities, of which 118 are known clusters. Cross-matching with recen catalogues yields five additional matches, leaving 40 likely new candidates absent from existing compilations. The estimated false-positive rate is below 5%. Conclusions. CANDiSC offers a robust and scalable approach for detecting stellar clusters in deep, near-infrared surveys, successfully recovering known systems and revealing new candidates in the obscured and crowded regions of the Galactic plane.