J/A+A/703/A100ivo://CDS.VizieR/J/A+A/703/A100doi:10.26093/cds/vizier/bc-nz/lbaaCDSNizovkina M.Larsen S.S.Brown A.G.A.Helmi A.2026-08-03T06:58:52Z2025-11-06T07:42:23ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.fropen-star-clustersmetallicityextinctionstellar-distancestellar-agesOpen clusters (OCs) are crucial objects for studying stellar evolution and Galactic dynamics due to the shared origin and composition of the stars within each cluster. The precision of cluster parameter determination has significantly improved with the availability of homogeneous photometric Gaia data; however, challenges such as age-metallicity degeneracy and lack of spectroscopic observations remain. We investigate whether metallicities derived from low-resolution Gaia XP spectra can be effectively used to break degeneracies and improve the accuracy of OC parameter determinations. We analysed 20 OCs using isochrone fitting methods on Gaia DR3 photometry and metallicity estimates from several Gaia XP-based catalogues. We derived age, distance modulus, and extinction using the Approximate Bayesian Computation (ABC). We compared the parameter estimates to the values obtained in other works through isochrone fitting with spectroscopically constrained metallicities or through neural network techniques applied only to the photometry. We found the systematic difference between Gaia XP derived metallicities and those obtained from high-resolution spectroscopy to be 0.1-0.15dex. We found a systematic age difference of <0.03+/-0.13dex compared to isochrone fitting using high-resolution spectroscopy, and <0.08+/-0.21dex compared to neural network-based methods, and a median individual error of ~0.065dex. Despite their low resolution, Gaia XP metallicities effectively constrain parameters of clusters lacking a well-populated RGB. When used with stringent quality cuts and incorporated as priors, they allow to determine ages comparable in precision to those based on high-resolution spectroscopy and more precise than photometry-only neural network methods. These results highlight the potential of Gaia data for accurate cluster parameter analysis and detailed Galactic studies without relying on traditional spectroscopy.2025A&A...703A.100Nhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/703/A100CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toI/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)J/A+A/585/A150 : On the metallicity of open clusters. III. (Netopil+, 2016)J/A+A/618/A93 : Gaia DR2 open clusters in the Milky Way (Cantat-Gaudin+, 2018)J/A+A/623/A108 : Age of 269 GDR2 open clusters (Bossini+, 2019)J/A+A/686/A42 : Improving the open cluster census. III. (Hunt+, 2024)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/703/A100https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/703/A100http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/703/A100https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/703/A100https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/703/A100http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/703/A100GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/703/A100/tableb (OC parameters derived in this study (tables B1 and B2))https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/703/A100/tableb?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/703/A100/tableb?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/703/A100/tableb?GETtext/xml+votable180.050000true169.36925862-62.734084780.0055555555555555566/1302 2395 4078 4298 4698 14752 15121 20797 21531 21557 28711 29269 30439 31812 37592 37920 37933 39775 39901 41834https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/703/A100?format=asciidefaultJ/A+A/703/A100/tablebOC parameters derived in this study (tables B1 and B2)recnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintClusterCluster namemeta.id;meta.maincharRA_ICRSRight ascension of densest point (ICRS) at Ep=2016.0 (from Hunt et al., 2024A&A...686A..42H, Cat. J/A+A/686/A42)degpos.eq.ra;meta.mainDE_ICRSDeclination of densest point (ICRS) at Ep=2016.0 (from Hunt et al., 2024A&A...686A..42H, Cat. J/A+A/686/A42)degpos.eq.dec;meta.mainr_[Fe/H]Reference for the metallicity assumed (1)meta.refchar[Fe/H]Cluster metallicity assumedlog(Sun)phys.abund.Zfloats_[Fe/H]? Standard deviation of [Fe/H] derived from Gaia XP-based catalogueslog(Sun)stat.stdev;phys.abund.FefloatnullableAVBayesian estimation of extinction in V bandmagphys.absorptionfloatAV1616th percentile of extinction in V bandmagphys.absorptionfloatAV8484th percentile of extinction in V bandmagphys.absorptionfloatDMBayesian estimation of distance modulusmagphot.mag.distModfloatDM1616th percentile of distance modulusmagphot.mag.distModfloatDM8484th percentile of distance modulusmagphot.mag.distModfloatlogAgeBayesian estimation of logarithm of cluster agelog(yr)time.agefloatlogAge1616th percentile of logarithm of cluster agelog(yr)time.agefloatlogAge8484th percentile of logarithm of cluster agelog(yr)time.agefloatSimbadSimbad column added by the CDSmeta.ref.urlchar