Context. Determination and calibration of the ages of stars, which heavily rely on stellar evolutionary models, are very challenging, while representing a crucial aspect in many astrophysical areas. Aims. We describe the methodologies that, taking advantage of Gaia-DR1 and the Gaia-ESO Survey data, enable the comparison of observed open star cluster sequences with stellar evolutionary models. The final, long-term goal is the exploitation of open clusters as age calibrators. Methods. We perform a homogeneous analysis of eight open clusters using the Gaia-DR1 TGAS catalogue for bright members and information from the Gaia-ESO Survey for fainter stars. Cluster membership probabilities for the Gaia-ESO Survey targets are derived based on several spectroscopic tracers. The Gaia-ESO Survey also provides the cluster chemical composition. We obtain cluster parallaxes using two methods. The first one relies on the astrometric selection of a sample of bona fide members, while the other one fits the parallax distribution of a larger sample of TGAS sources. Ages and reddening values are recovered through a Bayesian analysis using the 2MASS magnitudes and three sets of standard models. Lithium depletion boundary (LDB) ages are also determined using literature observations and the same models employed for the Bayesian analysis. Results. For all but one cluster, parallaxes derived by us agree with those presented in Gaia Collaboration (2017, A&A, 601, A19), while a discrepancy is found for NGC 2516; we provide evidence supporting our own determination. Inferred cluster ages are robust against models and are generally consistent with literature values. Conclusions. The systematic parallax errors inherent in the Gaia DR1 data presently limit the precision of our results. Nevertheless, we have been able to place these eight clusters onto the same age scale for the first time, with good agreement between isochronal and LDB ages where there is overlap. Our approach appears promising and demonstrates the potential of combining Gaia and ground-based spectroscopic datasets.

We present a catalogue of 73,221 white dwarf candidates extracted from the astrometric and photometric data of the recently published Gaia DR2 catalogue. White dwarfs were selected from the Gaia Hertzsprung-Russell diagram with the aid of the most updated population synthesis simulator. Our analysis shows that Gaia has virtually identified all white dwarfs within 100 pc from the Sun. Hence, our sub-population of 8,555 white dwarfs within this distance limit and the colour range considered, − 0.52 < (G BP − G RP ) < 0.80, is the largest and most complete volume-limited sample of such objects to date. From this sub-sample we identified 8,343 CO-core and 212 ONe-core white dwarf candidates and derived a white dwarf space density of 4.9±0.4×10 −3 pc −3 . A bifurcation in the Hertzsprung-Russell diagram for these sources, which our models do not predict, is clearly visible. We used the Virtual Observatory tool VOSA to derive effective temperatures and luminosities for our sources by fitting their spectral energy distributions, that we built from the UV to the NIR using publicly available photometry through the Virtual Observatory. From these parameters, we derived the white dwarf radii. Interpolating the radii and effective temperatures in hydrogen-rich white dwarf cooling sequences, we derived the surface gravities and masses. The Gaia 100 pc white dwarf population is clearly dominated by cool (∼ 8,000 K) objects and reveals a significant population of massive (M ∼ 0.8M ) white dwarfs, of which no more than ∼ 30−40 % can be attributed to hydrogen-deficient atmospheres, and whose origin remains uncertain.

Context. Stars are born together from giant molecular clouds and, if we assume that the priors were chemically homogeneous and well-mixed, we expect them to share the same chemical composition. Most of the stellar aggregates are disrupted while orbiting the Galaxy and most of the dynamic information is lost, thus the only possibility of reconstructing the stellar formation history is to analyze the chemical abundances that we observe today. Aims. The chemical tagging technique aims to recover disrupted stellar clusters based merely on their chemical composition. We evaluate the viability of this technique to recover co-natal stars that are no longer gravitationally bound. Methods. Open clusters are co-natal aggregates that have managed to survive together. We compiled stellar spectra from 31 old and intermediate-age open clusters, homogeneously derived atmospheric parameters, and 17 abundance species, and applied machine learning algorithms to group the stars based on their chemical composition. This approach allows us to evaluate the viability and efficiency of the chemical tagging technique. Results. We found that stars at different evolutionary stages have distinct chemical patterns that may be due to NLTE effects, atomic diffusion, mixing, and biases. When separating stars into dwarfs and giants, we observed that a few open clusters show distinct chemical signatures while the majority show a high degree of overlap. This limits the recovery of co-natal aggregates by applying the chemical tagging technique. Nevertheless, there is room for improvement if more elements are included and models are improved.