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The third data release of has provided stellar parameters, metallicity $ M H alpha Fe $, individual abundances broadening parameter from its Radial Velocity Spectrograph (RVS) spectra for about million objects thanks to the module, implemented in the Gaia pipeline. The catalogue also publishes the radial velocity of million sources. In recent years, many spectroscopic surveys with ground-based telescopes have been undertaken, including the public survey designed to be complementary to in particular towards faint stars We took advantage of the intersections between RVS and to compare their stellar parameters, abundances and radial and rotational velocities. We aimed at verifying the overall agreement between the two datasets, considering the various calibrations and the quality-control flag system suggested for the parameters. For the targets in common between RVS and we performed several statistical checks on the distributions of their stellar parameters, abundances and velocities of targets in common. For the Gaia surface gravity and metallicity we considered both the uncalibrated and calibrated values. Overall, there is a good agreement between the results of the two surveys. We find an excellent agreement between the and radial velocities given the uncertainties affecting each dataset. Less than out of the $ spectroscopic binaries are flagged as non-single stars by Gaia. For the effective temperature and in the bright regime ($G we found a very good agreement, with an absolute residual difference of about kelvin kelvin $) for the giant stars and of about kelvin kelvin $) for the dwarf stars; in the faint regime ($G we found a worse agreement, with an absolute residual difference of about kelvin kelvin $) for the giant stars and of about kelvin kelvin $) for the dwarf stars. For the surface gravity, the comparison indicates that the calibrated gravity should be preferred to the uncalibrated one. For the metallicity, we observe in both the uncalibrated and calibrated cases a slight trend whereby overestimates it at low metallicity; for $ M H and $ alpha Fe a marginally better agreement is found using the calibrated results; finally for the individual abundances (Mg, Si, Ca, Ti, S, Cr, Ni, Ce) our comparison suggests to avoid results with flags indicating low quality ($ XUncer or higher These remarks are in line with the ones formulated by gspspec. We confirm that the vbroad parameter is loosely correlated with the $v i$ for slow rotators. Finally, we note that the quality (accuracy, precision) of the parameters degrades quickly for objects fainter than $G 11$ or RVS We find that the somewhat imprecise abundances due to its medium-resolution spectroscopy over a short wavelength window and the faint $G$ regime of the sample under study can be counterbalanced by working with averaged quantities. We extended our comparison to star clusters using averaged abundances using not only the stars in common, but also the members of clusters in common between the two samples, still finding a very good agreement. Encouraged by this result, we studied some properties of the open-cluster population, using both and clusters: our combined sample traces very well the radial metallicity and $ Fe H $ gradients, the age-metallicity relations in different radial regions, and allows us to place the clusters in the thin disc.
The third data release of has provided stellar parameters, metallicity $ M H alpha Fe $, individual abundances broadening parameter from its Radial Velocity Spectrograph (RVS) spectra for about million objects thanks to the module, implemented in the Gaia pipeline. The catalogue also publishes the radial velocity of million sources. In recent years, many spectroscopic surveys with ground-based telescopes have been undertaken, including the public survey designed to be complementary to in particular towards faint stars We took advantage of the intersections between RVS and to compare their stellar parameters, abundances and radial and rotational velocities. We aimed at verifying the overall agreement between the two datasets, considering the various calibrations and the quality-control flag system suggested for the parameters. For the targets in common between RVS and we performed several statistical checks on the distributions of their stellar parameters, abundances and velocities of targets in common. For the Gaia surface gravity and metallicity we considered both the uncalibrated and calibrated values. Overall, there is a good agreement between the results of the two surveys. We find an excellent agreement between the and radial velocities given the uncertainties affecting each dataset. Less than out of the $ spectroscopic binaries are flagged as non-single stars by Gaia. For the effective temperature and in the bright regime ($G we found a very good agreement, with an absolute residual difference of about kelvin kelvin $) for the giant stars and of about kelvin kelvin $) for the dwarf stars; in the faint regime ($G we found a worse agreement, with an absolute residual difference of about kelvin kelvin $) for the giant stars and of about kelvin kelvin $) for the dwarf stars. For the surface gravity, the comparison indicates that the calibrated gravity should be preferred to the uncalibrated one. For the metallicity, we observe in both the uncalibrated and calibrated cases a slight trend whereby overestimates it at low metallicity; for $ M H and $ alpha Fe a marginally better agreement is found using the calibrated results; finally for the individual abundances (Mg, Si, Ca, Ti, S, Cr, Ni, Ce) our comparison suggests to avoid results with flags indicating low quality ($ XUncer or higher These remarks are in line with the ones formulated by gspspec. We confirm that the vbroad parameter is loosely correlated with the $v i$ for slow rotators. Finally, we note that the quality (accuracy, precision) of the parameters degrades quickly for objects fainter than $G 11$ or RVS We find that the somewhat imprecise abundances due to its medium-resolution spectroscopy over a short wavelength window and the faint $G$ regime of the sample under study can be counterbalanced by working with averaged quantities. We extended our comparison to star clusters using averaged abundances using not only the stars in common, but also the members of clusters in common between the two samples, still finding a very good agreement. Encouraged by this result, we studied some properties of the open-cluster population, using both and clusters: our combined sample traces very well the radial metallicity and $ Fe H $ gradients, the age-metallicity relations in different radial regions, and allows us to place the clusters in the thin disc.
With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. To this end, we built a stable gradient-boosted random-forest regressor ( xgboost ), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and $12<!PCT!>$ in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters $A_V$ and $T_ eff $, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars).
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