Consensus based Algorithm for Nonparametric Detection of Star Clusters (CANDiSC)

TL;DR

This paper introduces CANDiSC, an automated, consensus-based algorithm that effectively detects star clusters in crowded, obscured regions of the Galactic plane using near-infrared survey data, improving over previous visual methods.

Contribution

The paper presents a novel nonparametric, consensus framework combining multiple density estimation methods for systematic star cluster detection in large survey data.

Findings

Detected 163 stellar overdensities, including 40 new candidates.

Achieved false-positive rate below 5%.

Successfully recovered known clusters and identified new candidates.

Abstract

Context: The VISTA Variables in the Via Lactea (VVV) and its extension (VVVX) are near-infrared surveys mapping the Galactic bulge and adjacent disk. These data have enabled the discovery of numerous star clusters obscured by high and spatially variable extinction. 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 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, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and nearest-neighbour density estimation within a consensus framework. A stellar overdensity is classified as a candidate if identified by at least two of these methods. We apply CANDiSC to 680 tiles in the VVVX PSF photometric catalogue, covering approximately 1100 square degrees. Results: We detect 163 stellar overdensities, of which 118 are known clusters. Cross-matching with recent catalogues yields five additional matches, leaving 40 likely new candidates absent from existing compilations. The estimated false-positive rate is below 5 percent. 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.