Search

We used the Javalambre Photometric Local Universe Survey (J-PLUS) DR2 photometry in twelve optical bands over 2176 deg2 to estimate the fraction of white dwarfs with presence of CaII H+K absorption along the cooling sequence. We compared the J-PLUS photometry against metal-free theoretical models to estimate the equivalent width in the J0395 passband of 10 nm centered at 395 nm (EW_J0395), a proxy to detect calcium absorption. A total of 4399 white dwarfs within 30000 > Teff > 5500 K and mass M > 0.45 Msun were analyzed. Their EW_J0395 distribution was modeled using two populations, corresponding to polluted and non-polluted systems, to estimate the fraction of calcium white dwarfs (f_Ca) as a function of Teff. The probability for each individual white dwarf of presenting calcium absorption, pca, was also computed. The comparison with both the measured Ca/He abundance and the metal pollution from spectroscopy shows that EW_J0395 correlates with the presence of calcium. The fraction of calcium white dwarfs increases from f_Ca = 0 at Teff = 13500 K to f_Ca = 0.15 at Teff = 5500 K. We compare our results with the fractions derived from the 40 pc spectroscopic sample and from SDSS spectra. The trend found in J-PLUS observations is also present in the 40 pc sample, however SDSS shows a deficit of metal-polluted objects at Teff < 12000 K. Finally, we found 39 white dwarfs with pca > 0.99. Twenty of them have spectra presented in previous studies, whereas we observed six additional targets. These 26 objects were all confirmed as metal-polluted systems. The J-PLUS optical data provide a robust statistical measurement for the presence of CaII H+K absorption in white dwarfs. We find a 15 +- 3 % increase in the fraction of calcium white dwarfs from Teff = 13500 K to 5500 K, which reflects their selection function in the optical from the total population of metal-polluted systems., Comment: Submitted to Astronomy and Astrophysics. Comments are welcome

With its 12 optical filters, the Javalambre-Photometric Local Universe Survey (J-PLUS) provides an unprecedented multicolor view of the local Universe. The third data release (DR3) covers 3,192 deg$^2$ and contains 47.4 million objects. However, the classification algorithms currently implemented in its pipeline are deterministic and based solely on the sources morphology. Our goal is classify the sources identified in the J-PLUS DR3 images into stars, quasi-stellar objects (QSOs), and galaxies. For this task, we present BANNJOS, a machine learning pipeline that uses Bayesian neural networks to provide the probability distribution function (PDF) of the classification. BANNJOS is trained on photometric, astrometric, and morphological data from J-PLUS DR3, Gaia DR3, and CatWISE2020, using over 1.2 million objects with spectroscopic classification from SDSS DR18, LAMOST DR9, DESI EDR, and Gaia DR3. Results are validated using $1.4 10^5$ objects and cross-checked against theoretical model predictions. BANNJOS outperforms all previous classifiers in terms of accuracy, precision, and completeness across the entire magnitude range. It delivers over 95% accuracy for objects brighter than $r = 21.5$ mag, and ~90% accuracy for those up to $r = 22$ mag, where J-PLUS completeness is < 25%. BANNJOS is also the first object classifier to provide the full probability distribution function (PDF) of the classification, enabling precise object selection for high purity or completeness, and for identifying objects with complex features, like active galactic nuclei with resolved host galaxies. BANNJOS has effectively classified J-PLUS sources into around 20 million galaxies, 1 million QSOs, and 26 million stars, with full PDFs for each, which allow for later refinement of the sample. The upcoming J-PAS survey, with its 56 color bands, will further enhance BANNJOS's ability to detail each source's nature., Comment: Submitted to Astronomy and Astrophysics. 29 pages, 23 figures, 4 tables

The Gaia-ESO Survey is an European Southern Observatory (ESO) public spectroscopic survey that targeted $10^5$ stars in the Milky Way covering the major populations of the disk, bulge and halo. The observations were made using FLAMES on the VLT obtaining both UVES high ($R\sim47,000$) and GIRAFFE medium ($R\sim20,000$) resolution spectra. The analysis of the Gaia-ESO spectra was the work of multiple analysis teams (nodes) within five working groups (WG). The homogenisation of the stellar parameters within WG11 (high resolution observations of FGK stars) and the homogenisation of the stellar parameters within WG10 (medium resolution observations of FGK stars) is described here. In both cases, the homogenisation was carried out using a bayesian Inference method developed specifically for the Gaia-ESO Survey by WG11. The WG10 homogenisation primarily used the cross-match of stars with WG11 as the reference set in both the stellar parameter and chemical abundance homogenisation. In this way the WG10 homogenised results have been placed directly onto the WG11 stellar parameter and chemical abundance scales. The reference set for the metal-poor end was sparse which limited the effectiveness of the homogenisation in that regime. For WG11, the total number of stars for which stellar parameters were derived was 6,231 with typical uncertainties for Teff, log g and [Fe/H] of 32~K, 0.05 and 0.05 respectively. One or more chemical abundances out of a possible 39 elements were derived for 6,188 of the stars. For WG10, the total number of stars for which stellar parameters were derived was 76,675 with typical uncertainties for Teff, log g and [Fe/H] of 64~K, 0.15 and 0.07 respectively. One or more chemical abundances out of a possible 30 elements were derived for 64,177 of the stars., Comment: 31 pages, 19 figures

The Gaia-ESO Survey (GES) is a large public spectroscopic survey which acquired spectra for more than 100000 stars across all major components of the Milky Way. In addition to atmospheric parameters and stellar abundances that have been derived in previous papers of this series, the GES spectra allow us to detect spectroscopic binaries with one (SB1), two (SB2) or more (SBn $\ge$ 3) components. Cross-correlation functions (CCFs) have been re-computed thanks to a dozen spectral masks probing a range of effective temperatures, surface gravities and metallicities. By optimising the mask choice for a given spectrum, the new computed so-called Nacre (Narrow cross-correlation experiment) CCFs are narrower and allow to unblend more stellar components than standard masks. The Doe (Detection of Extrema) extremum-finding code then selects the individual components and provides their radial velocities. From the sample of HR10 and HR21 spectra corresponding to 37565 objects, the present study leads to the detection of 322 SB2, ten (three of them being tentative) SB3, and two tentative SB4. In particular, compared to our previous study, the Nacre CCFs allow us to multiply the number of SB2 candidates by $\approx$ 1.5. The colour-magnitude diagram reveals, as expected, the shifted location of the SB2 main sequence. A comparison between the SB identified in Gaia DR3 and the ones detected in the present work is performed and the complementarity of the two censuses is discussed. An application to mass-ratio determination is presented, and the mass-ratio distribution of the GES SB2 is discussed. When accounting for the SB2 detection rate, an SB2 frequency of $\approx$ 1.4% is derived within the present stellar sample of mainly FGK-type stars. As primary outliers identified within the GES data, SBn spectra produce a wealth of information and useful constraints for the binary population synthesis studies.

The characterization of white dwarf atmospheres is crucial for accurately deriving stellar parameters such as effective temperature, mass, and age. We aim to classify the population of white dwarfs up to 500 pc into hydrogen-rich or hydrogen-deficient atmospheres based on Gaia spectra and to derive an accurate spectral type-temperature distribution of white dwarfs as a function of the effective temperature for the largest observed unbiased sample of these objects. We took advantage of the recent Gaia low-resolution spectra available for 76,657 white dwarfs up to 500 pc. We calculated synthetic J-PAS narrow-band photometry and fitted the spectral energy distribution of each object with up-to-date models for hydrogen-rich and helium-rich white dwarf atmospheres. We estimated the probability for a white dwarf to have a hydrogen-rich atmosphere and validated the results using the Montreal White Dwarf Database. Finally, precise effective temperature values were derived for each object using La Plata evolutionary models. We have successfully classified a total of 65,310 white into DAs and non-DAs with an accuracy of 94%. An unbiased subsample of nearly 34,000 objects was built, from which we computed a precise spectral distribution spanning an effective temperature range from 5,500 to 40,000 K, while accounting for potential selection effects. Some characteristic features of the spectral evolution, such as the deficit of helium-rich stars at T_eff $\approx$35,000-40,000 K and in the range 22,000 < T_eff < 25,000 K, as well as a gradual increase from 18,000K to T_eff $\approx$7,000K, where the non-DA stars percentage reaches its maximum of 41%, followed by a decrease for cooler temperatures, are statistically significant. These findings will provide precise constraints for the proposed models of spectral evolution., Comment: 7 pages, 11 pages. Accepted for publication in A&A

The Gaia-ESO Survey is a public spectroscopic survey that has targeted $\gtrsim10^5$ stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its public final release in May 2022. Unlike other spectroscopic surveys, Gaia-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O ($T_\mathrm{eff} \sim 30,000-52,000$~K) all the way to K-M ($\gtrsim$3,500~K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different type. In the final data release (internal data release 6) of the Gaia-ESO Survey, we provide the final database containing a large number of products such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, e.g., activity and accretion indicators in young stars and membership probability in star clusters for more than 114,000 stars. The spectral analysis is coordinated by a number of Working Groups (WGs) within the Survey, which specialise in the various stellar samples. Common targets are analysed across WGs to allow for comparisons (and calibrations) amongst instrumental setups and spectral types. Here we describe the procedures employed to ensure all Survey results are placed on a common scale to arrive at a single set of recommended results for all Survey collaborators to use. We also present some general quality and consistency checks performed over all Survey results., Comment: A&A accepted, minor revision, 36 pages, 38 figures

We present the photometric calibration of the twelve optical passbands for the Javalambre Photometric Local Universe Survey (J-PLUS) third data release (DR3), comprising 1642 pointings of two square degrees each. We selected nearly 1.5 million main sequence stars with a signal-to-noise ratio larger than ten in the twelve J-PLUS passbands and available low-resolution (R = 20-80) spectrum from the blue and red photometers (BP/RP) in Gaia DR3. We compared the synthetic photometry from BP/RP spectra with the J-PLUS instrumental magnitudes, after correcting for the magnitude and color terms between both systems, to obtain an homogeneous photometric solution for J-PLUS. To circumvent the current limitations in the absolute calibration of the BP/RP spectra, the absolute color scale was derived using the locus of 109 white dwarfs closer than 100 pc with a negligible interstellar extinction. Finally, the absolute flux scale was anchored to the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) photometry in the r band. The precision of the J-PLUS photometric calibration, estimated from duplicated objects observed in adjacent pointings and by comparison with the spectro-photometric standard star GD 153, is ~12 mmag in u, J0378, and J0395; and ~7 mmag in J0410, J0430, g, J0515, r, J0660, i, J0861, and z. The estimated accuracy in the calibration along the surveyed area is better than 1% for all the passbands. The Gaia BP/RP spectra provide a high-quality, homogeneous photometric reference in the optical range across the full-sky, in spite of their current limitations as an absolute reference. The calibration method for J-PLUS DR3 reaches an absolute precision and accuracy of 1% in the twelve optical filters within an area of 3284 square degrees., Comment: Submitted to Astronomy and Astrophysics. 17 pages, 14 figures, 2 appendix. Comments are welcome. arXiv admin note: text overlap with arXiv:2101.12407

The third data release of Gaia has provided low resolution spectra for ~100,000 white dwarfs (WDs) that, together with the excellent photometry and astrometry, represent an unrivalled benchmark for the study of this population. In this work, we first built a highly-complete volume-limited sample consisting in 12,718 WDs within 100 pc from the Sun. The use of VOSA tool allowed us to perform an automated fitting of their spectral energy distributions to different atmospheric models. In particular, the use of spectrally derived J-PAS photometry from Gaia spectra led to the classification of DA and non-DA WDs with an accuracy >90%, tested in already spectroscopically labelled objects. The excellent performance achieved was extended to practically the whole population of WDs with effective temperatures above 5500 K. Our results show that, while the A branch of the Gaia WD Hertzsprung-Russell diagram is practically populated by DA WDs, the B branch is largely formed by non-DAs (65%). The remaining 35% of DAs within the B branch implies a second peak at ~0.8 Mo in the DA-mass distribution. Additionally, the Q branch and its extension to lower temperatures can be observed for both DA and non-DA objects due to core crystallisation. Finally, we derived a detailed spectral evolution function, which confirms a slow increase of the fraction of non-DAs as the effective temperature decreases down to 10,500 K, where it reaches a maximum of 36% and then decreases for lower temperatures down to ~31%., Comment: Accepted for publication in MNRAS

With a unique set of 54 overlapping narrow-band and two broader filters covering the entire optical range, the incoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will provide a great opportunity for stellar physics and near-field cosmology. In this work, we use the miniJPAS data in 56 J-PAS filters and 4 complementary SDSS-like filters to explore and prove the potential of the J-PAS filter system in characterizing stars and deriving their atmospheric parameters. We obtain estimates for the effective temperature with a good precision (<150 K) from spectral energy distribution fitting. We have constructed the metallicity-dependent stellar loci in 59 colours for the miniJPAS FGK dwarf stars, after correcting certain systematic errors in flat-fielding. The very blue colours, including uJAVA-r, J0378-r, J0390-r, uJPAS-r, show the strongest metallicity dependence, around 0.25 mag/dex. The sensitivities decrease to about 0.1 mag/dex for the J0400-r, J0410-r, and J0420-r colours. The locus fitting residuals show peaks at the J0390, J0430, J0510, and J0520 filters, suggesting that individual elemental abundances such as [Ca/Fe], [C/Fe], and [Mg/Fe] can also be determined from the J-PAS photometry. Via stellar loci, we have achieved a typical metallicity precision of 0.1 dex. The miniJPAS filters also demonstrate strong potential in discriminating dwarfs and giants, particularly the J0520 and J0510 filters. Our results demonstrate the power of the J-PAS filter system in stellar parameter determinations and the huge potential of the coming J-PAS survey in stellar and Galactic studies., Comment: 16 pages, 16 figures, 3 tables, accepted by MNRAS

Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar population and brown dwarfs, from M7 V to cooler objects with L, T, and Y spectral types. Most of them have been discovered using wide-field imaging surveys, for which the Virtual Observatory (VO) has proven to be of great utility. We aim to perform a search for UCDs in the entire Javalambre Photometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$) following a VO methodology. We also explore the ability to reproduce this search with a purely machine learning (ML)-based methodology that relies solely on J-PLUS photometry. We followed three different approaches based on parallaxes, proper motions, and colours, respectively, using the VOSA tool to estimate the effective temperatures. For the ML methodology, we built a two-step method based on principal component analysis and support vector machine algorithms. We identified a total of 7827 new candidate UCDs, which represents an increase of about 135% in the number of UCDs reported in the sky coverage of the J-PLUS second data release. Among the candidate UCDs, we found 122 possible unresolved binary systems, 78 wide multiple systems, and 48 objects with a high Bayesian probability of belonging to a young association. We also identified four objects with strong excess in the filter corresponding to the Ca II H and K emission lines and four other objects with excess emission in the H$\alpha$ filter. With the ML approach, we obtained a recall score of 92% and 91% in the test and blind test, respectively. We consolidated the proposed search methodology for UCDs, which will be used in deeper and larger upcoming surveys such as J-PAS and Euclid. We concluded that the ML methodology is more efficient in the sense that it allows for a larger number of true negatives to be discarded prior to analysis with VOSA, although it is more photometrically restrictive., Comment: Accepted in A&A

With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances are necessary for a large range of evolutionary stages, metallicities, and Galactic volume. In the age of stellar parametrization on industrial scales, spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely infer stellar labels (atmospheric parameters and abundances). To prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we aim to apply machine learning techniques to lithium measurements and analyses. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (Teff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atm parameters and lithium abundances for ~40,000 stars. We show that the CNN properly learns the physics of the stellar labels, from relevant spectral features through a broad range of evolutionary stages and stellar parameters. The Li feature at 6707.8 A is successfully singled out by our CNN, among the thousands of lines. Rare objects such as Li-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample. The CNN approach is very well adapted for the next generations of spectroscopic surveys aimed at studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST Milky Way disk and bulge low- and high-resolution) surveys. In this context, the caveats of ML applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances., Comment: A&A, accepted 05 December 2022, 23 pages, 23 figures. Codes and trained models available at https://github.com/SamirNepal/Li\_CNN\_2022

We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral features in a physically meaningful way. We built a neural network and trained it on GIRAFFE spectra with associated stellar labels from the sixth internal Gaia-ESO data release. Our neural network predicts the atmospheric parameters Teff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe], and [Fe/H] for 30115 stellar spectra. The scatter of predictions from eight slightly different network models shows a high internal precision of the network results: 24 K for Teff, 0.03 for log(g), 0.02 dex for [Mg/Fe], 0.03 dex for [Al/Fe], and 0.02 dex for [Fe/H]. The network gradients reveal that the network is inferring the labels in a physically meaningful way from spectral features. Validation with benchmark stars and several scientific applications confirm that our network predictions are accurate for individual stars and recover the properties of different stellar populations in the Milky Way galaxy. Such a study provides very good insights into the application of machine-learning for the spectral analysis of large-scale spectroscopic surveys, such as WEAVE and 4MIDABLE-LR and -HR (4MOST Milky Way disk and bulge low- and high-resolution). The community will have to put a substantial effort into building proactive training sets for machine-learning methods to minimize the possible systematics., Comment: Submitted to A&A

The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100,000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper (arXiv:2206.02901) introduces the survey results. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. The Gaia-ESO Survey obtained 202,000 spectra of 115,000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022., Comment: 38 pages. A&A in press

A renewed interest about the origin of \emph{r}-process elements has been stimulated by the multi-messenger observation of the gravitational event GW170817, with the detection of both gravitational waves and electromagnetic waves corresponding to the merger of two neutron stars. Such phenomenon has been proposed as one of the main sources of the \emph{r}-process. However, the origin of the \emph{r}-process elements at different metallicities is still under debate. We aim at investigating the origin of the \emph{r}-process elements in the Galactic thin disc population. From the sixth internal data release of the \emph{Gaia}-ESO we have collected a large sample of Milky Way thin- and thick-disc stars for which abundances of Eu, O, and Mg are available. The sample consists of members of 62 open clusters, located at a Galactocentric radius from $\sim 5$ kpc to $\sim 20$ kpc in the disc, in the metallicity range $[-0.5, 0.4]$ and covering an age interval from 0.1 to 7 Gy, and about 1300 Milky Way disc field stars in the metallicity range $[-1.5, 0.5]$. We compare the observations with the results of a chemical evolution model, in which we varied the nucleosynthesis sources for the three considered elements. Our main result is that Eu in the thin disc is predominantly produced by sources with short lifetimes, such as magneto-rotationally driven SNe. There is no strong evidence for additional sources at delayed times. Our findings do not imply that there cannot be a contribution from mergers of neutron stars in other environments, as in the halo or in dwarf spheroidal galaxies, but such a contribution is not needed to explain Eu abundances at thin disc metallicities., Comment: 20 pages, accepted for publication in A&A

In the last 15 years different ground-based spectroscopic surveys have been started (and completed) with the general aim of delivering stellar parameters and elemental abundances for large samples of Galactic stars, complementing Gaia astrometry. Among those surveys, the Gaia-ESO Public Spectroscopic Survey (GES), the only one performed on a 8m class telescope, was designed to target 100,000 stars using FLAMES on the ESO VLT (both Giraffe and UVES spectrographs), covering all the Milky Way populations, with a special focus on open star clusters. This article provides an overview of the survey implementation (observations, data quality, analysis and its success, data products, and releases), of the open cluster survey, of the science results and potential, and of the survey legacy. A companion article (Gilmore et al.) reviews the overall survey motivation, strategy, Giraffe pipeline data reduction, organisation, and workflow. The GES has determined homogeneous good-quality radial velocities and stellar parameters for a large fraction of its more than 110,000 unique target stars. Elemental abundances were derived for up to 31 elements for targets observed with UVES. Lithium abundances are delivered for about 1/3 of the sample. The analysis and homogenisation strategies have proven to be successful; several science topics have been addressed by the Gaia-ESO consortium and the community, with many highlight results achieved. The final catalogue has been released through the ESO archive at the end of May 2022, including the complete set of advanced data products. In addition to these results, the Gaia-ESO Survey will leave a very important legacy, for several aspects and for many years to come., Comment: Accepted for publication in Astronomy and Astrophysics. 30 pages, 30 figures, 4 tables

Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The J-PLUS is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools to efficiently analyse large data sets, such as the one from J-PLUS, and enable us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a SVR algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. We then label them with the stellar effective temperature, the surface gravity and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach in order to fully take into account the uncertainties of both the magnitudes and stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the Root Mean Square Error of 160K in the effective temperature regression, 0.35 in the surface gravity regression and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods., Comment: 18 pages, 10 figures, accepted by AA

The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars that formed within the same stellar association. This idea - known as chemical tagging - has not produced the expected results, especially within the thin disk where open stellar clusters have chemical patterns that are difficult to disentangle. The ultimate goal of this study is to probe the feasibility of chemical tagging within the thin disk population using high-quality data from a controlled sample of stars. We also aim at improving the existing techniques of chemical tagging and giving guidance on different strategies of clustering analysis in the elemental abundance space. Here we develop the first blind search of open clusters' members through clustering analysis in the elemental abundance space using the OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate different strategies of analysis, determining which ones are more performing. Second, we apply these methods to a data set including both field stars and open clusters attempting a blind recover of as many open clusters as possible. We show how specific strategies of data analysis can improve the final results. Specifically, we demonstrate that open clusters can be more efficaciously recovered with the Manhattan metric and on a space whose dimensions are carefully selected. Using these (and other) prescriptions we are able to recover open clusters hidden in our data set and find new members of these stellar associations. Our results indicate that there are chances of recovering open clusters' members via clustering analysis in the elemental abundance space. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms., Comment: Accepted for publication by A&A. 21 pages, 11 figures

We analyze the white dwarf population in miniJPAS, the first square degree observed with 56 medium-band, 145 A in width optical filters by the Javalambre Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a data-based forecast for the white dwarf science with low-resolution (R ~ 50) photo-spectra. We define the sample of the bluest point-like sources in miniJPAS with r < 21.5 mag, point-like probability larger than 0.5, (u-r) < 0.80 mag, and (g-i) < 0.25 mag. This sample comprises 33 sources with spectroscopic information, 11 white dwarfs and 22 QSOs. We estimate the effective temperature (Teff), the surface gravity, and the composition of the white dwarf population by a Bayesian fitting to the observed photo-spectra. The miniJPAS data permit the classification of the observed white dwarfs into H-dominated and He-dominated with 99% confidence, and the detection of calcium absorption and polluting metals down to r ~ 21.5 mag at least for sources with 7000 < Teff < 22000 K, the temperature range covered by the white dwarfs in miniJPAS. The effective temperature is estimated with a 2% uncertainty, close to the 1% from spectroscopy. A precise estimation of the surface gravity depends on the available parallax information. In addition, the white dwarf population at Teff > 7000 K can be segregated from the bluest extragalactic QSOs, providing a clean sample based on optical photometry alone. The J-PAS low-resolution photo-spectra provide precise and accurate effective temperatures and atmospheric compositions for white dwarfs, complementing the data from Gaia. J-PAS will also detect and characterize new white dwarfs beyond the Gaia magnitude limit, providing faint candidates for spectroscopic follow up., Comment: Submitted to Astronomy and Astrophyscis. 12 pages, 9 figures, 4 tables. Comments are welcome

The Gaia-ESO Survey (GES) is a large public spectroscopic survey that has collected, over a period of 6 years, spectra of ~ 10^5 stars. This survey provides not only the reduced spectra, but also the stellar parameters and abundances resulting from the analysis of the spectra. The GES dataflow is organised in 19 working groups. Working group 13 (WG13) is responsible for the spectral analysis of the hottest stars (O, B and A type, with a formal cut-off of Teff > 7000 K) that were observed as part of GES. We present the procedures and techniques that have been applied to the reduced spectra, in order to determine the stellar parameters and abundances of these stars. The procedure used is similar to that of other working groups in GES. A number of groups (called `Nodes') each independently analyse the spectra, using their state-of-the-art techniques and codes. Specific for the analysis in WG13 is the large temperature range that is covered (Teff = 7000 - 50,000 K), requiring the use of different analysis codes. Most Nodes can therefore only handle part of the data. Quality checks are applied to the results of these Nodes by comparing them to benchmark stars, and by comparing them one to another. For each star the Node values are then homogenised into a single result: the recommended parameters and abundances. Eight Nodes each analysed (part of) the data. In total 17,693 spectra of 6462 stars were analysed, most of them in 37 open star clusters. The homogenisation led to stellar parameters for 5584 stars. Abundances were determined for a more limited number of stars. Elements studied are He, C, N, O, Ne, Mg, Al, Si and Sc. Abundances for at least one of those elements were determined for 292 stars. The hot-star data analysed here, as well as the Gaia-ESO Survey data in general, will be of considerable use in future studies of stellar evolution and open clusters., Comment: 26 pages, 14 figures, accepted for publication in Astronomy & Astrophysics; language-edited version; two appendices merged

The last decade has seen a revolution in our knowledge of the Galaxy thanks to the Gaia and asteroseismic space missions and the ground-based spectroscopic surveys. To complete this picture, it is necessary to map the ages of its stellar populations. During recent years, the dependence on time of abundance ratios involving slow (s) neutron-capture and $\alpha$ elements (called chemical-clocks) has been used to provide estimates of stellar ages, usually in a limited volume close to the Sun. We aim to analyse the relations of chemical clocks in the Galactic disc extending the range to R$_{\rm GC}\sim$6-20~kpc. Using the sixth internal data release of the Gaia-ESO survey, we calibrated several relations between stellar ages and abundance ratios [s/$\alpha$] using a sample of open clusters, the largest one so far used with this aim. Thanks to their wide galactocentric coverage, we investigated the radial variations of the shape of these relations, confirming their non-universality. We estimated our accuracy and precision in recovering the global ages of open clusters, and the ages of their individual members. We applied the multi-variate relations with the highest correlation coefficients to the field star population. We confirm that there is no single age-chemical clock relationship valid for the whole disc, but that there is a dependence on the galactocentric position, which is related to the radial variation of the star formation history combined with the non-monotonic dependence on metallicity of the yields of the s-process elements from low- and intermediate-mass stars. Finally, the abundance ratios [Ba/$\alpha$] are more sensitive to age than those with [Y/$\alpha$] for young disc stars, and their slopes vary less with galactocentric distance., Comment: 14 pages, 10 figures + Appendix (3 tables and 2 figures)

The $Gaia$ mission has provided an unprecedented wealth of information about the white dwarf population of our Galaxy. In particular, our studies show that the sample up to 100\,pc from the Sun can be considered as practically complete. This fact allows us to estimate a precise fraction of double-degenerate ($1.18\pm 0.10$\%) and white dwarf plus main-sequence stars ($6.31\pm0.23$\%) among all white dwarfs through comoving pairs identification. With the aid of a detailed population synthesis code we are able to reproduce synthetic white dwarf populations with nearly identical fractions as the observed ones, thus obtaining valuable information about the binary fraction, $f_{\rm b}$, initial mass ratio distribution, $n(q)$, and initial separation distribution, $f(a)$, among other parameters. Our best-fit model is achieved within a $1\sigma$ confidence level for $f(a)\propto a^{-1}$, $n(q)\propto q^{n_q}$, with $n_q=-1.13^{+0.12}_{-0.10}$ and $f_{\rm b}=0.32\pm 0.02$. The fraction of white dwarf mergers generated by this model is $9\sim16\%$, depending on the common-envelope treatment. As sub-products of our modelling we find that around $1\sim3\%$ of the white dwarf population are unresolved double-degenerates and that only $\sim1\%$ of all white dwarfs contain a He-core. Finally, only a mild kick during white dwarf formation seems to be necessary for fitting the observed sky separation of double-degenerate systems., Comment: 14 pages, 8 figures, 5 tables, accepted for publication in MNRAS

The Gaia-ESO Survey (GES) is a public, high-resolution spectroscopic survey with FLAMES@VLT. GES targeted in particular a large sample of open clusters (OCs) of all ages. The different kinds of OCs are useful to reach the main science goals, which are the study of the OC structure and dynamics, the use of OCs to constrain and improve stellar evolution models, and the definition of Galactic disc properties (e.g. metallicity distribution). GES is organised in 19 working groups (WGs). We describe here the work of three of them, WG4 in charge of the selection of the targets within each cluster), WG1 responsible for defining the most probable candidate members, and WG6 in charge of the preparation of the observations. As GES has been conducted before Gaia DR2, we could not make use of the Gaia astrometry to define cluster members. We made use of public and private photometry to select the stars to be observed with FLAMES. Candidate target selection was based on ground-based proper motions, radial velocities, and X-ray properties when appropriate, and it was mostly used to define the position of the clusters' evolutionary sequences in the colour-magnitude diagrams. Targets for GIRAFFE were selected near the sequences in an unbiased way. We used available information on membership only for the few UVES stars. We collected spectra for 62 confirmed OCs (a few more were taken from the ESO archive). Among them are very young clusters, where the main targets are pre-main sequence stars, clusters with very hot and massive stars currently on the main sequence, intermediate-age and old clusters where evolved stars are the main targets. The selection of targets was as inclusive and unbiased as possible and we observed a representative fraction of all possible targets, thus collecting the largest, most accurate, and most homogeneous spectroscopic data set on ever achieved. [abridged], Comment: Accepted for publication on A&A. Abstract abridged from the accepted version

Pre-main sequence models with inflated radii are needed to simultaneously reproduce the colour-magnitude diagram and the lithium depletion pattern in young open clusters. We tested a new set of PMS models including radius inflation due to starspots or magnetic inhibition of convection, using five clusters observed by the Gaia-ESO Survey, spanning the age range ~10-100 Myr where such effects could be important. Gaia-ESO radial velocities were combined with Gaia EDR3 astrometry to obtain clean lists of high-probability members for the five clusters. A Bayesian maximum likelihood method was adopted to derive the best model parameters and the cluster reddening and age. Models were calculated for different values of the mixing length parameter ($\alpha_{ML}=2.0$, 1.5 and 1.0), without spots or with effective spot coverage $\beta_{spot}=0.2$ and 0.4. To reproduce the CMD and the Li depletion pattern in Gamma Vel A and B and in 25 Ori we need both a reduced convection efficiency $\alpha_{ML}=1.0$ and an effective spot coverage of ~20%. We obtained ages of 18 Myr and 21 Myr for Gamma Vel A and B, respectively, and 19 Myr for 25 Ori. However, a single isochrone is not sufficient to account for the Li dispersion, and an increasing level of spot coverage as mass decreases seems to be required. The older clusters (NGC2451B at 30 Myr, NGC2547 at 35 Myr, and NGC2516 at 138 Myr) are consistent with standard models ($\alpha_{ML}=2.0$ and no spots) except at low masses: a 20% spot coverage seems to reproduce the sequence of M-type stars better and might explain the observed abundance spread. The quality of Gaia-ESO data combined with Gaia allows us to gain important insights on PMS evolution. Models including starspots can provide a consistent explanation of the cluster sequences and Li abundances of young clusters, although a range of starspot coverage is required to fully reproduce the data., Comment: Accepted for publication in A&A, 18 pages, 10 figures. Revised version after language editing, revised acknowledgements