J/A+A/705/A244      CANDiSC cluster detections in VVVX            (Obasi+, 2026)

Consensus-based Detection of the nonparametric detection of star clusters (CANDiSC). Obasi C.O., Fernandez-Trincado J.G., Gomez M., Minniti D., Alonso-Garcia J., Ferreira B.P.L., Garro E.R., Dias B., Saito R.K., Barbuy B., Parisi M.C., Palma T., Tang B., Ortigoza-Urdaneta M., Baravalle L.D., Alonso M.V., Mauro F. <Astron. Astrophys. 705, A244 (2026)> =2026A&A...705A.244O 2026A&A...705A.244O (SIMBAD/NED BibCode)
ADC_Keywords: Infrared sources ; Photometry ; Surveys Keywords: Galaxy: bulge - Galaxy: disk - Galaxy: general - globular clusters: general - open clusters and associations: general Abstract: The VISTA Variables in the Via Lactea (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. Most previous searches relied on visual inspection of individual tiles, which is inefficient and biased against faint or low-density systems. We aim to develop an automated and 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. We introduce the Consensus-based Algorithm for Nonparametric Detection of Star Clusters (CANDISC), which integrates kernel density estimation (KDE), density-based spatial clustering (DBSCAN), and nearest-neighbour density estimation (NNDE) within a consensus framework. A stellar overdensity is classified as a candidate when 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. We detect 163 stellar overdensities, of which 118 correspond to known clusters. Cross-matching with recent catalogues yields five additional matches, leaving 40 likely new candidates not present in previous compilations. The estimated false-positive rate is below five percent. 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. Description: This catalogue compiles star cluster candidates identified in the VVV and VVVX near-infrared surveys, including those found with the CANDISC detection algorithm. CANDISC integrates three nonparametric density estimators (KDE, DBSCAN, and NNDE) within a consensus framework. A stellar overdensity is retained as a candidate when detected by at least two of the three methods. The input dataset consists of 680 VVVX PSF-photometry tiles reprocessed with DoPHOT (Alonso-Garcia et al., in prep.), covering approximately 1100 square degrees of the Galactic disk outside the inner bulge. The compilation of known VVV/VVVX clusters contains 788 entries (table1). CANDISC recovers 163 overdensities (table2), of which 118 correspond to known clusters and 40 appear to be new candidates not previously catalogued (table3). File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file tablea3.dat 41 788 Compilation of known VVV/VVVX clusters tablea2.dat 73 163 CANDISC detections with cross-match flags tablea1.dat 61 40 New cluster candidates detected in VVVX -------------------------------------------------------------------------------- See also: II/376 : VISTA Variable in the Via Lactea Survey (VVV) DR4.2 (Minniti+, 2023) J/MNRAS/481/3902 : New Galactic clusters in the VVVX disc area (Borissova+, 2018) J/A+A/689/A148 : VVVX tile coordinates (Saito+, 2024) J/ApJS/262/7 : 886 nearby star clusters (He+, 2022) J/ApJS/265/12 : Neighboring open clusters with Gaia DR3 (Qin+, 2023) J/A+A/673/A114 : Improving the open cluster census. II. (Hunt+, 2023) Byte-by-byte Description of file: tablea3.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 15 A15 --- Cluster Cluster identifier 17- 28 F12.7 deg RAdeg Right ascension (ICRS) 30- 41 F12.7 deg DEdeg Declination (ICRS) -------------------------------------------------------------------------------- Byte-by-byte Description of file: tablea2.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 12 A12 --- Tile VVV/VVVX tile name 14- 25 F12.7 deg RAdeg Right ascension (ICRS) 27- 38 F12.7 deg DEdeg Declination (ICRS) 40- 73 A34 --- LitName Literature counterpart name -------------------------------------------------------------------------------- Byte-by-byte Description of file: tablea1.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 15 A15 --- Cluster Cluster identifier 17- 22 A6 --- Tile VVVX tile name 24- 33 F10.3 deg RAdeg Right ascension (ICRS) 35- 45 F11.3 deg DEdeg Declination (ICRS) 47- 49 I3 --- A Count (0.4-1.4 mag) 51- 53 I3 --- B Count (0.3-1.4 mag) 55- 57 I3 --- C Count (0.6-2.0 mag) 59- 61 I3 --- D Count (0.5-1.4 mag) -------------------------------------------------------------------------------- Acknowledgements: From Casmir Obasi, casmiroluabuchukwuobasi(at)gmail.com This work was funded by the Postdoctoral Talent Attraction Competition for Research Centers and Institutes of the Universidad Andres Bello (UNAB) 2025. Support was provided under grant No. DI-07-25/ATP.
(End) Patricia Vannier [CDS] 05-Dec-2025
The document above follows the rules of the Standard Description for Astronomical Catalogues; from this documentation it is possible to generate f77 program to load files into arrays or line by line