Refining open cluster parameters with Gaia XP metallicities

Context. Open 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. Aims. 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. Methods. We analysed 20 OCs using isochrone fitting methods on Gaia DR3 photometry and metallicity estimates from several Gaia XP-based catalogues. We used the synthetic cluster method, implemented through ASteCA, to derive age, distance modulus, and extinction using the approximate Bayesian computation (ABC). Results. We compared the parameter estimates of these open clusters 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 0.1–0.15 dex. We found a systematic age difference of <0.03 ± 0.13 dex compared to isochrone fitting using high-resolution spectroscopy, and <0.08 ± 0.21 dex compared to neural network-based methods, and a median individual error, σ_log(age), of ~0.065 dex. Conclusions. Metallicities from Gaia XP spectra prove highly effective in constraining the parameters of open clusters that do not have a populated red giant branch (RGB), despite the low spectral resolution. When used with stringent quality cuts and incorporated as priors, they make it possible to determine ages comparable in precision to those based on high-resolution spectroscopy. Moreover, compared to studies of open clusters that use neural networks without metallicity constraints, our method yields smaller uncertainties. These results highlight the potential of Gaia data for accurate cluster parameter analysis and detailed Galactic studies, even in the absence of traditional spectroscopic observations.