|Authors:||I. Negueruela 1, A. de Burgo 2|
|Affiliations:||(1) Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Alicante, San Vicente del Raspeig, Spain; (2) Instituto de Astrofísica de Canarias & Universidad de La Laguna, Dpto. Astrofísica, La Laguna, Tenerife, Spain|
|Accepted by:||Astronomy & Astrophysics|
Automated analysis of Gaia astrometric data has led to the discovery of many new high-quality open cluster candidates. With a good determination of their parameters, these objects become excellent tools to investigate the properties of our Galaxy. We explore whether young open clusters can be readily identified from Gaia data alone by studying the properties of their Gaia colour-magnitude diagrams. We also want to compare the results of a traditional cluster analysis with those of automated methods. We selected three young open cluster candidates from the UBC catalogue, ranging from a well-populated object with a well-defined sequence to a poorly-populated, poorly-defined candidate. We obtained classification spectra for the brightest stars in each. We redetermined members based on EDR3 data and fitted isochrones to derive age, distance and reddening. All three candidates are real clusters with age below 100 Ma. UBC103 is a moderately populous cluster, with an age around 70 Ma. At a distance of $\sim$3 kpc, it forms a binary cluster with the nearby NGC6683. UBC114 is a relatively nearby ($\sim$1.5 kpc) poorly-populated cluster containing two early-B stars. UBC587 is a dispersed, very young ($<$10 Ma) cluster located at $\sim$3 kpc, behind the Cygnus~X region, and may be a valuable tracer of the Orion arm. The OCfinder methodology for the identification of new open clusters is extremely successful, with even poor candidates resulting in interesting detections. The presence of an almost vertical photometric sequence in the Gaia colour-magnitude diagram is a safe way to identify young open clusters. Automated methods for the determination of cluster properties give approximate solutions, but are still subject to some difficulties. There is some evidence suggesting that artificial intelligence systems may systematically underestimate extinction, which may impact in the age determination.