arXiv subjects

F. Jiménez-Esteban

Publications and source records attributed to F. Jiménez-Esteban.

At least 19 recordsLinked to original sources

arXiv2026

Stellar characterization with photometric colors from J-PLUS and 2MASS surveys

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Two Micron All-Sky Survey (2MASS). Methods. Our method consists of a semi-supervised machine learning approach based on the k-means method combined with a modified k-nearest neighbors algorithm. This method compares the observed photometry to a set of reference data to estimate the stellar effective temperature ($T_{\rm eff}$), surface gravity ($\log{g}$), and metallicity ([Fe/H]) of stars from J-PLUS Data Release 3 (DR3). Results. We estimated $T_{\rm eff}$, $\log{g}$, and [Fe/H], for approximately 5.6 million stars from J-PLUS DR3, along with their errors.Our results were in agreement with spectroscopic estimates from LAMOST and APOGEE.We also applied a dimension reduction method, seeking greater efficiency by reducing the computation time and minimizing the needed information for calculating the stellar parameters, resulting in a subset of 11 colors. From this approach, stellar parameters were obtained for approximately six million stars. Conclusions. Our results demonstrated the potential of using a method built from machine learning algorithms that do not require prior training. Additionally, it was shown that the proposed method allowed estimating reliable atmospheric parameters even when the available photometry did not fulfill all photometric quality criteria. We defined a neighborhood parameter, which assesses the reliability of our estimations and indicates that objects with smaller neighborhoods values have lower uncertainties.

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arXiv2025

J-VAR: Analysis of RR Lyrae light curves in seven optical bands

Context:RR Lyrae stars, with their accurate period and amplitude measurements, constrain stellar evolution and map Galactic structure. The Javalambre VARiability (J-VAR) survey is the time-domain extension of the Javalambre Photometric Local Universe survey, which provides time-series data across seven optical bands, including $gri$ and four medium and narrow bands. Aims: Our goal is to construct and analyze light curves for RR Lyrae stars identified in the J-VAR's first data release using the \textit{Gaia} third data release (DR3) Variable Stars catalog as a reference. Methods: The light curves of $315$ RR Lyrae were analyzed by fitting templates from the Sloan Digital Sky Survey Multiband Template Library. The periods and amplitudes for the seven bands in J-VAR were independently obtained from the best-fitted templates. Results: The J-VAR periods show strong agreement with \textit{Gaia} DR3 values. The Bailey diagram for each J-VAR filter shows larger pulsation amplitudes at bluer wavelengths. Amplitudes, after normalizing by the $r$-band amplitude, show an exponential trend, with the bluer J-VAR filter centered at $395~\text{nm}$ having twice the amplitude of the reddest J-VAR passband at $861~\text{nm}$. The normalized amplitudes of the RR Lyrae stars from {\it Gaia} and the Zwicky Transient Facility are consistent with the J-VAR trend. Finally, the SDSS templates derived from broadbands also provide a proper description for the medium and narrow band light curves. Conclusions: The J-VAR RR Lyrae catalog offers reliable pulsation parameters and light curves in seven optical filters, allowing the systematic study of amplitude trends from $395~\text{nm}$ to $860~\text{nm}$.

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arXiv2025

J-PLUS Reconstructing the Milky Way Disc's star formation history with twelve-filter photometry

Wide-field, multi-filter photometric surveys enable the reconstruction of the Milky Way's star formation history (SFH) on Galactic scales and offer new insights into disc assembly. The twelve-filter system of the Javalambre Photometric Local Universe Survey (J-PLUS) is particularly suitable, as its colours trace stellar chemical abundances and help alleviate the age-metallicity degeneracy in colour-magnitude diagram fitting. We aim to recover the SFH of the Galactic disc and separate its chemically distinct components by combining J-PLUS DR3 photometry with Gaia astrometry. We also evaluate the potential of isochrone fitting to estimate stellar ages and metallicities as proxies for evolutionary trends. We fit magnitudes and parallaxes of $1.38\times10^{6}$ stars using a Bayesian multiple isochrone method. The bright region of the colour-absolute-magnitude diagram ($M_{r}\leq4.2$ mag) constrains ages, while the faint region provides an empirical metallicity prior mitigating the age-metallicity degeneracy. Both PARSEC and BaSTI isochrones, in solar-scaled and $α$-enhanced forms, are adopted. The recovered SFH reveals two sequences: an $α$-enhanced population forming rapidly between $12.5$ and $8$ Gyr ago, enriching from [M/H]$\sim-0.6$ to $0.1$ dex; and a solar-scaled sequence emerging $\sim8$ Gyr ago, dominating after $\sim7$ Gyr with slower enrichment reaching solar metallicity by $3$ Gyr. Metal-rich ([M/H]$\gtrsim0$) stars are confined to $\vert z_{GC}\vert\lesssim1$ kpc, whereas metal-poor ([M/H]$\lesssim-0.5$) stars reach $\vert z_{GC}\vert\sim2$ kpc. Simultaneous fitting of both isochrone families reveals distinct formation epochs for the thin and thick discs. J-PLUS multi-filter photometry, combined with Gaia parallaxes, mitigates age-metallicity degeneracies and enables detailed mapping of the Milky Way's temporal and chemical evolution.

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arXiv2024

The Gaia-ESO Survey: The DR5 analysis of the medium-resolution GIRAFFE and high-resolution UVES spectra of FGK-type stars

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.

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arXiv2023

The Gaia-ESO Survey: new spectroscopic binaries in the Milky Way

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.

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arXiv2023

The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning

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.

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arXiv2022

The miniJPAS survey: stellar atmospheric parameters from 56 optical filters

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.

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arXiv2022

J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology

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$α$ 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.

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arXiv2022

J-PLUS: Support Vector Regression to Measure Stellar Parameters

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.

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arXiv2022

The Gaia-ESO survey: placing constraints on the origin of r-process elements

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.

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arXiv2022

The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters blindly recovered in the elemental abundance space

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.

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arXiv2022

The Gaia-ESO Survey: Age-chemical-clock relations spatially resolved in the Galactic disc

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 $α$ 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$6-20~kpc. Using the sixth internal data release of the Gaia-ESO survey, we calibrated several relations between stellar ages and abundance ratios [s/$α$] using a sample of open clusters, the largest one so far used with this aim. Thanks to their wide galactocentric coverage, we investigated the radial variations of the shape of these relations, confirming their non-universality. We estimated our accuracy and precision in recovering the global ages of open clusters, and the ages of their individual members. We applied the multi-variate relations with the highest correlation coefficients to the field star population. We confirm that there is no single age-chemical clock relationship valid for the whole disc, but that there is a dependence on the galactocentric position, which is related to the radial variation of the star formation history combined with the non-monotonic dependence on metallicity of the yields of the s-process elements from low- and intermediate-mass stars. Finally, the abundance ratios [Ba/$α$] are more sensitive to age than those with [Y/$α$] for young disc stars, and their slopes vary less with galactocentric distance.

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arXiv2021

Gaia-ESO Survey: Role of magnetic activity and starspots on pre-main sequence lithium evolution

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 ($α_{ML}=2.0$, 1.5 and 1.0), without spots or with effective spot coverage $β_{spot}=0.2$ 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 $α_{ML}=1.0$ 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 ($α_{ML}=2.0$ 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.

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arXiv2021

The Gaia-ESO Survey: Membership probabilities for stars in 63 open and 7 globular clusters from 3D kinematics

Spectroscopy from the final internal data release of the Gaia-ESO Survey (GES) has been combined with Gaia EDR3 to assign membership probabilities to targets observed towards 63 Galactic open clusters and 7 globular clusters. The membership probabilities are based chiefly on maximum likelihood modelling of the 3D kinematics of the targets, separating them into cluster and field populations. From 43211 observed targets, 13985 are identified as highly probable cluster members ($P>0.9$), with an average membership probability of 0.993. The addition of GES radial velocities successfully drives down the fraction of false positives and we achieve better levels of discrimination in most clusters over the use of astrometric data alone, especially those at larger distances. Since the membership selection is almost purely kinematic, the union of this catalogue with GES and Gaia is ideal for investigating the photometric and chemical properties of clusters as a function of stellar mass, age and Galactic position.

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arXiv2021

J-PLUS: Searching for very metal-poor star candidates using the SPEEM pipeline

We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential to identify low-metallicity stars using the Stellar Parameters Estimation based on Ensemble Methods (SPEEM) pipeline. SPEEM is a tool to provide determinations of atmospheric parameters for stars and separate stellar sources from quasars, using the unique J-PLUS photometric system. The adoption of adequate selection criteria allows the identification of metal-poor star candidates suitable for spectroscopic follow-up. SPEEM consists of a series of machine learning models which uses a training sample observed by both J-PLUS and the SEGUE spectroscopic survey. The training sample has temperatures Teff between 4\,800 K and 9\,000 K; $\log g$ between 1.0 and 4.5, and $-3.1<[Fe/H]<+0.5$. The performance of the pipeline has been tested with a sample of stars observed by the LAMOST survey within the same parameter range. The average differences between the parameters of a sample of stars observed with SEGUE and J-PLUS, which were obtained with the SEGUE Stellar Parameter Pipeline and SPEEM, respectively, are $ΔTeff\sim 41$ K, $Δ\log g\sim 0.11$ dex, and $Δ[Fe/H]\sim 0.09$ dex. A sample of 177 stars have been identified as new candidates with $[Fe/H]<-2.5$ and 11 of them have been observed with the ISIS spectrograph at the William Herschel Telescope. The spectroscopic analysis confirms that $64\%$ of stars have $[Fe/H]<-2.5$, including one new star with $[Fe/H]<-3.0$. SPEEM in combination with the J-PLUS filter system has shown the potential to estimate the stellar atmospheric parameters (Teff, $\log g$, and [Fe/H]). The spectroscopic validation of the candidates shows that SPEEM yields a success rate of $64\%$ on the identification of very metal-poor star candidates with $[Fe/H]<-2.5$.

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arXiv2019

J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools

Ultracool dwarfs (UCDs) are objects with spectral types equal or later than M7. Most of them have been discovered using wide-field imaging surveys. The Virtual Observatory (VO) has proven to be of great utility to efficiently exploit these astronomical resources. We aim to validate a VO methodology designed to discover and characterize UCDs in the J-PLUS photometric survey. J-PLUS is a multiband survey carried out with the wide angle T80Cam optical camera mounted on the 0.83-m telescope JAST/T80 in the Observatorio Astrofísico de Javalambre. In this work we make use of the Internal Data Release (IDR) covering 528 deg$^2$. We complement J-PLUS photometry with other catalogues in the optical and IR using VOSA, a VO tool that estimates physical parameters from the spectral energy distribution fitting to collections of theoretical models. Objects identified as UCDs are distinguished from background M giants and highly reddened stars using parallaxes and proper motions from Gaia DR2. We identify 559 UCDs, ranging from i=16.2 to 22.4 mag, of which 187 are candidate UCDs not previously reported in the literature. This represents an increase in the number of known UCDs of about 50% in the studied region of the sky, particularly at the faint end of our sensitivity, which is interesting as reference for future wide and deep surveys such as Euclid. Three candidates constitute interesting targets for exoplanet surveys because of their proximity (<40 pc). We also analyze the kinematics of UCDs in our catalogue and find evidence that it is consistent with a Galactic thin-disk population, except for 6 objects that might be members of the thick disk. The results obtained validate the proposed methodology, which will be used in future J-PLUS and J-PAS releases. Considering the region of the sky covered by the IDR used, we foresee to discover 3,000-3,500 new UCDs at the end of the J-PLUS project.

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arXiv2018

The Gaia-ESO survey: impact of extra-mixing on C- and N-abundances of giant stars

The GES survey using FLAMES at the VLT has obtained high-resolution UVES spectra for a large number of giant stars, allowing a determination of the abundances of the key chemical elements C and N at their surface. The surface abundances of these chemical species are well-known to change in stars during their evolution on the red giant branch after the first dredge-up episod, as a result of extra-mixing phenomena. We investigate the effects of thermohaline mixing on C and N abundances using the first comparison between the GES [C/N] determinations with simulations of the observed fields using a model of stellar population synthesis. We explore the effects of thermohaline mixing on the chemical properties of giants through stellar evolutionary models computed with the stellar evolution code STAREVOL. We include these stellar evolution models in the Besançon Galaxy model to simulate the [C/N] distributions determined from the UVES spectra of the GES and compare them with the observations. Theoretical predictions including the effect of thermohaline mixing are in good agreement with the observations. However, the field stars in the GES with C and N-abundance measurements have a metallicity close to solar, where the efficiency of thermohaline mixing is not very large. The C and N abundances derived by the GES in open and globular clusters clearly show the impact of thermohaline mixing at low-metallicity, allowing to explain the [C/N] ratio observed in lower-mass and older giant stars. Using independent observations of carbon isotopic ratio in clump field stars and open clusters, we also confirm that thermohaline mixing should be taken into account to explain the behavior of 12C/13C ratio as a function of stellar age. Overall the current model including thermohaline mixing is able to reproduce very well the C- and N-abundances over the whole metallicity range investigated by the GES data.

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arXiv2017

The $Gaia$-ESO Survey: the inner disk intermediate-age open cluster NGC 6802

Milky Way open clusters are very diverse in terms of age, chemical composition, and kinematic properties. Intermediate-age and old open clusters are less common, and it is even harder to find them inside the solar Galactocentric radius, due to the high mortality rate and strong extinction inside this region. NGC 6802 is one of the inner disk open clusters (IOCs) observed by the $Gaia$-ESO survey (GES). This cluster is an important target for calibrating the abundances derived in the survey due to the kinematic and chemical homogeneity of the members in open clusters. Using the measurements from $Gaia$-ESO internal data release 4 (iDR4), we identify 95 main-sequence dwarfs as cluster members from the GIRAFFE target list, and eight giants as cluster members from the UVES target list. The dwarf cluster members have a median radial velocity of $13.6\pm1.9$ km s$^{-1}$, while the giant cluster members have a median radial velocity of $12.0\pm0.9$ km s$^{-1}$ and a median [Fe/H] of $0.10\pm0.02$ dex. The color-magnitude diagram of these cluster members suggests an age of $0.9\pm0.1$ Gyr, with $(m-M)_0=11.4$ and $E(B-V)=0.86$. We perform the first detailed chemical abundance analysis of NGC 6802, including 27 elemental species. To gain a more general picture about IOCs, the measurements of NGC 6802 are compared with those of other IOCs previously studied by GES, that is, NGC 4815, Trumpler 20, NGC 6705, and Berkeley 81. NGC 6802 shows similar C, N, Na, and Al abundances as other IOCs. These elements are compared with nucleosynthetic models as a function of cluster turn-off mass. The $α$, iron-peak, and neutron-capture elements are also explored in a self-consistent way.

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