arXiv subjects

G. Guiglion

Publications and source records attributed to G. Guiglion.

At least 19 recordsLinked to original sources

arXiv2025

Probing the origins. II. Unravelling lithium depletion and stellar motion: Intrinsic stellar properties drive depletion, not kinematics

In Paper I, we classified a stellar sample from the thin disc with a broad range in metallicity as being churned outward or inward, or blurred/undisturbed. In this paper (Paper II), we delve deeper by analysing our entire metallicity-stratified sample along with their dynamic properties, focusing on the connection between radial migration and Li depletion. We analyse the chemo-dynamics of a set of 1188 thin disc dwarf stars observed by the \textit{Gaia}-ESO survey, previously classified into six metallicity-stratified groups via Hierarchical Clustering (HC). We examine several features, such as effective temperatures, masses, and dynamic properties. We also implement a parametric survival analysis using penalised splines (logistic distribution) to quantify how stellar properties and motion (or migration) direction jointly influence Li depletion patterns. We find that stars in our sample that appear to have churned outward are predominantly Li-depleted, regardless of their metallicities. These stars are also the oldest, coldest, and least massive compared to those in the same HC group that have either churned inward or kept their orbital radii. Our survival analysis confirms temperature as the primary driver of Li depletion, followed by metallicity and age, while migration direction shows negligible influence. The increasing proportion of outward-churned stars with higher metallicity (and older ages) indicates their dominant influence on the overall trend observed in the [Fe/H]-A(Li) space for stellar groups with [Fe/H]>0. The survival model reinforces that the observed Li depletion stems primarily from intrinsic stellar properties (cool temperatures, higher metallicity, old ages) rather than migration history. This suggests the metallicity-dependent depletion pattern emerges through stellar evolution rather than Galactic dynamical processes.

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arXiv2025

Survey of Surveys. II. Stellar parameters for 23 millions of stars

In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a project to critically compile survey results in a single catalogue, facilitating the scientific use of the available information. In this second release, we present two new catalogs of stellar parameters (Teff, logg, and [Fe/H]). To build the first catalog, SoS-Spectro, we calibrated internally and externally stellar parameters from five spectroscopic surveys (APOGEE, GALAH, Gaia-ESO, RAVE, and LAMOST) and externally on the PASTEL database. The second catalog, SoS-ML catalog, is obtained by using SoS-Spectro as a reference to train a multi-layer perceptron, which predicts stellar parameters based on two photometric surveys, SDSS and SkyMapper. As a novel approach, we build on previous parameters sets, from Gaia DR3 and Andrae et al. (2023), aiming to improve their precision and accuracy. We obtain a catalog of stellar parameters for around 23 millions of stars, which we make publicly available. We validate our results with several comparisons with other machine learning catalogs, stellar clusters, and astroseismic samples. We find substantial improvements in the parameters estimates compared to other Machine Learning methods in terms of precision and accuracy, especially in the metal-poor range, as shown in particular when validating our results with globular clusters. We believe that there are two reasons behind our improved results at the low-metallicity end: first, our use of a reference catalog, the SoS-Spectro, which is calibrated using high-resolution spectroscopic data; and second, our choice to build on pre-existing parameter estimates from em Gaia and Andrae et al., rather than attempting to obtain our predictions from survey data alone.

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arXiv2025

Impact of <3D> NLTE on GCE of oxygen with the RAdial Velocity Experiment

Stellar abundances, coupled with kinematics are a unique way to understand the chemo-dynamical processes that occurred to build the Milky Way and its local volume as we observe today. However, measuring abundances is challenging as one needs to properly address the effect of departure from the Local Thermodynamic Equilibrium (LTE), as well as the commonly used 1-dimensional model atmosphere. In this work, we constrain the chemical evolution of [O/Fe] in FG stars of the RAVE survey with [O/Fe] abundances derived in non-LTE (NLTE) and with horizontally-temporally-averaged 3D (<3D>) model atmospheres. Using standard spectral fitting method, we determine for the first time LTE and NLTE [O/Fe] ratios from the O triplet at 8446A in turn-off and dwarf stars thanks to intermediate-resolution RAVE spectra, assuming both 1D and <3D> model atmosphere. NLTE effects play a significant role when determining oxygen even at a resolution of R= 7500. Typical NLTE-LTE corrections of the order of -0.12 dex are measured in dwarfs and turn-off stars using 1D MARCS models. In contrast to applying <3D> NLTE abundance corrections or the classical 1D LTE, the full <3D> NLTE spectral fitting yields improving the precision of abundances by nearly 10%. We show that the decrease of [O/Fe] in the super-solar [Fe/H] regime is rather characterised by a flat trend when [O/Fe] is computed in <3D> NLTE from full spectral fitting. We attribute this flattening at super-solar [Fe/H] to the interplay between locally born stars with negative [O/Fe] and stars migrated from the inner MW regions with super-solar [O/Fe], supporting the complex chemo-dynamical history of the Solar neighbourhood. Our results are key for understanding the effects of <3D> and NLTE when measuring [O/Fe]. This work is a test bed for the analysis of 4MOST low-resolution spectra that will share similar properties as RAVE in the red wavelength domain.

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arXiv2025

Realising the potential of large spectroscopic surveys with machine-learning

Machine-learning is playing an increasing role in helping the astronomical community to face data analysis challenges, in particular in the field of Galactic Archaeology and large scale spectroscopic surveys. We present recent developments in the field of convolutional neural-networks (CNNs) for stellar abundances in the context of the Galactic spectroscopic surveys Gaia-ESO, and Gaia-RVS. Especially, by combining the full Gaia data product, we manage to characterize for the first time the [alpha/M] vs. [M/H] bimodality in the Galactic disc with Gaia-RVS spectra at low-S/N. This work is highly relevant for the next generation of large scale surveys such as MSE, 4MOST, and WST.

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arXiv2025

The interplay between super-metallicity, lithium depletion, and radial migration in nearby stars

We report the discovery of a peculiar set of old super-metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between $\sim$ 0.5-1.5 kpc observed by the \emph{Gaia}-ESO Survey. These stars show lithium (Li) depletion, which is anti-correlated with their [Fe/H]. To investigate these stars' chemo-dynamical properties, we used data from the \emph{Gaia}-ESO Survey. We applied hierarchical clustering to group the stars based on their abundances (excluding Li). Orbits were integrated using \emph{Gaia} astrometry and radial velocities from \emph{Gaia}-ESO. Our analysis suggests that the high metallicity of these stars is incompatible with their formation in the solar neighbourhood. We also found that their Li envelope abundance is below the benchmark meteoritic value, in agreement with previous works. This result supports the idea that the Li abundance in old, super-metal-rich dwarf stars should not be considered a proxy for the local interstellar medium Li.

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arXiv2024

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

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. 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 $12\%$ 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 $T_{\rm eff}$, 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). [abridged]

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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

Beyond Gaia DR3: Tracing the [$α$/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks

In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. However, one-third of the published spectra have 15<S/N<25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining Gaia-RVS spectra with the full set of Gaia products and high-resolution, high-quality ground-based spectroscopic reference datasets. We developed a hybrid convolutional neural network (CNN) that combines the Gaia DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g) as well as overall [M/H]) and chemical abundances ([Fe/H] and [α/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N=15. We managed to characterise the [α/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of Gaia-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry.

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arXiv2023

Observational constraints on the origin of the elements. VIII. Constraining the Barium, Strontium and Yttrium chemical evolution in metal-poor stars

Recently Lian et al. (2023), thanks to Gaia-ESO data, studied the chemical evolution of neutron-capture elements in the regime [Fe/H]>-1. We aim here to complement this study down to [Fe/H]=-3, and focus on Ba, Y, Sr, and abundance ratios of [Ba/Y] and [Sr/Y], which give comprehensive views on s-process nucleosynthesis channels. We measured LTE and NLTE abundances of Ba, Y, and Sr in 323 Galactic metal-poor stars using high-resolution optical spectra with high S/N. We used the spectral fitting code TSFitPy, together with 1D model atmospheres using previously determined LTE and NLTE atmospheric parameters. The NLTE effects are on the order of -0.1 to ~0.2dex depending on the element. T he ratio between heavy and light s-process elements [Ba/Y] varies weakly with [Fe/H] even in the metal-poor regime, consistently with the behavior in the metal-rich regime. The [Ba/Y] scatter at a given metallicity is larger than the abundance measurement uncertainties. Homogeneous chemical evolution models with different yields prescriptions are unable to accurately reproduce the [Ba/Y] scatter at low-[Fe/H]. Adopting the stochastic chemical evolution model by Cescutti & Chaippini (2014) allows to reproduce the observed scatter in the abundance pattern of [Ba/Y] and [Ba/Sr]. With our observations, we rule out the need for an arbitrary scaling of the r-process contribution as previously suggested by the model authors. We have showed how important it is to properly include NLTE effects when measuring chemical abundances, especially in the metal-poor regime. This work shows that the choice of the Galactic chemical evolution model (stochastic vs. 1-zone) is key when comparing models to observations. The upcoming surveys such as 4MOST and WEAVE will deliver high quality spectra of many thousands of metal-poor stars, and this work gives a typical case study of what could be achieved with such surveys.

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arXiv2023

The Gaia-ESO Survey: Empirical estimates of stellar ages from lithium equivalent widths (EAGLES)

We present an empirical model of age-dependent photospheric lithium depletion, calibrated using a large, homogeneously-analysed sample of 6200 stars in 52 open clusters, with ages from 2--6000 Myr and $-0.3<{\rm [Fe/H}]<0.2$, observed in the Gaia-ESO spectroscopic survey. The model is used to obtain age estimates and posterior age probability distributions from measurements of the Li I 6708A equivalent width for individual (pre) main sequence stars with $3000 < T_{\rm eff}/{\rm K} <6500$, a domain where age determination from the HR diagram is either insensitive or highly model-dependent. In the best cases, precisions of 0.1 dex in log age are achievable; even higher precision can be obtained for coeval groups and associations where the individual age probabilities of their members can be combined. The method is validated on a sample of exoplanet-hosting young stars, finding agreement with claimed young ages for some, but not others. We obtain better than 10 per cent precision in age, and excellent agreement with published ages, for seven well-studied young moving groups. The derived ages for young clusters ($<1$ Gyr) in our sample are also in good agreement with their training ages, and consistent with several published, model-insensitive lithium depletion boundary ages. For older clusters there remain systematic age errors that could be as large as a factor of two. There is no evidence to link these errors to any strong systematic metallicity dependence of (pre) main sequence lithium depletion, at least in the range $-0.29 < {\rm [Fe/H]} < 0.18$. Our methods and model are provided as software -- "Empirical AGes from Lithium Equivalent widthS" (EAGLES).

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arXiv2023

The Gaia-ESO Survey: homogenisation of stellar parameters and elemental abundances

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.

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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 Gaia-ESO Survey: Probing the lithium abundances in old metal-rich dwarf stars in the Solar vicinity

We test a scenario in which radial migration could affect the Li abundance pattern of dwarf stars in the solar neighbourhood. This may confirm that the Li abundance in these stars can not serve as a probe for the Li abundance in the interstellar medium. We use the high-quality data (including Li abundances) from the 6th internal Data Release of the Gaia-ESO survey. In this sample, we group stars by similarity in chemical abundances via hierarchical clustering. Our analysis treats both measured Li abundances and upper limits. The Li envelope of the previously identified radially migrated stars is well below the benchmark meteoritic value (<3.26 dex); the star with the highest detected abundance has A(Li) = 2.76 dex. This confirms the previous trends observed for old dwarf stars (median ages $\sim$ 8 Gyr), where Li decreases for [Fe/H]$\gtrsim$0. This result acts as supporting evidence that the abundance of Li measured in the upper envelope of old dwarf stars should not be considered a proxy for the interstellar medium Li. Our scenario also indicates that the stellar yields for [M/H]>0 should not be decreased, as recently proposed in the literature. Our study backs the recent studies that claimed that old dwarfs on the hot side of the dip are efficient probes of the ISM abundance of Li, provided atomic diffusion does not lower significantly the initial Li abundance in the atmospheres of metal-rich objects.

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arXiv2022

The Gaia-ESO Survey: Old super-metal-rich visitors from the inner Galaxy

We report the identification of a set of old super metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between ~0.5-1.5 kpc. We discuss their properties to understand their origins. We use data from the internal data release 6 of the Gaia-ESO Survey. We selected stars observed at high resolution with abundances of 21 species of 18 individual elements. We apply hierarchical clustering to group the stars with similar chemical abundances within the complete chemical abundance space. According to their chemical properties, this set of super metal-rich stars can be arranged into five subgroups. Four seem to follow a chemical enrichment flow, where nearly all abundances increase in lockstep with Fe. The fifth subgroup shows different chemical characteristics. All subgroups have the following features: median ages of the order of 7-9 Gyr, Solar or sub-Solar [Mg/Fe] ratios, maximum height between 0.5-1.5 kpc, low eccentricities, and a detachment from the expected metallicity gradient with guiding radius. The high metallicity of our stars is incompatible with a formation in the Solar neighbourhood. Their dynamic properties agree with theoretical expectations that these stars travelled from the inner Galaxy due to blurring and, most importantly, to churning. We suggest that most of this population's stars originated in the Milky Way's inner regions (inner disc and/or the bulge) and later migrated to the Solar neighbourhood. The region from where the stars originated had a complex chemical enrichment history, with contributions from supernovae types Ia and II and possibly asymptotic giant branch stars.

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arXiv2022

The Gaia-ESO Survey: Lithium measurements and new curves of growth

The Gaia-ESO Survey (GES) is a large public spectroscopic survey that was carried out using the multi-object FLAMES spectrograph at the Very Large Telescope. The survey provides accurate radial velocities, stellar parameters, and elemental abundances for ~115,000 stars in all Milky Way components. In this paper we describe the method adopted in the final data release to derive lithium equivalent widths (EWs) and abundances. Lithium EWs were measured using two different approaches for FGK and M-type stars, to account for the intrinsic differences in the spectra. For FGK stars, we fitted the lithium line using Gaussian components, while direct integration over a predefined interval was adopted for M-type stars. Care was taken to ensure continuity between the two regimes. Abundances were derived using a new set of homogeneous curves of growth that were derived specifically for GES, and which were measured on a synthetic spectral grid consistently with the way the EWs were measured. The derived abundances were validated by comparison with those measured by other analysis groups using different methods. Lithium EWs were measured for ~40,000 stars, and abundances could be derived for ~38,000 of them. The vast majority of the measures (80%) have been obtained for stars in open cluster fields. The remaining objects are stars in globular clusters, or field stars in the Milky Way disc, bulge, and halo. The GES dataset of homogeneous lithium abundances described here will be valuable for our understanding of several processes, from stellar evolution and internal mixing in stars at different evolutionary stages to Galactic evolution.

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arXiv2022

The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with Machine-Learning

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.

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arXiv2022

The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

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.

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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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