Young massive stellar clusters (YMSCs) have emerged as potential γ-ray sources after the recent association of a dozen YMSCs with extended γ-ray emission. The large size of the detected halos, comparable to that of the wind-blown bubble expected around YMSCs, makes the γ-ray detection of individual YMSCs rather challenging. As a result, the emission from most of the Galactic YMSCs could be unresolved, thus contributing to the diffuse γ-ray radiation observed along the Galactic Plane. In this study, we estimate the possible contribution to the Galactic diffuse γ-ray emission from a synthetic population of YMSCs, and we compare it with observations obtained with different experiments, from 1 GeV to hundreds of teraelectronvolt in three regions of the Galactic Plane. As the population of galactic YMSCs is only known locally, we evaluated the contribution of γ-ray emission relying on the simulation of synthetic populations of YMSCs based on the observed properties of local clusters. We computed the γ-ray emission from each cluster assuming that the radiation is purely hadronic in nature and produced by cosmic rays that are accelerated at the cluster's collective wind termination shock. We find that the γ-ray emission from unresolved YMSCs can significantly contribute to the observed Galactic diffuse flux, especially in the inner part of the Galaxy, and that an important role is played by kinetic power injected by the Wolf-Rayet stellar winds. The predicted γ-ray flux should be considered as a lower limit, given that our calculation does not include the contribution of supernovae exploding in YMSCs.

Stellar magnetic activity, manifested through spots (faculae and flares), fundamentally shapes the exoplanets' environments. For low-mass stars in particular, where most habitable-zone planets reside, the variable magnetic phenomena can dominate atmospheric chemistry, surface radiation levels, long-term atmospheric escape, and ultimately habitability. However, physical characteristics of these transients (e.g. energy and temperature) and their spectra remain ill-constrained due to limitations in cadence and magnitude access of current spectroscopic facilities. A next-generation 12-m class ground-based observatory equipped with integral-field spectroscopy (IFS) and multi-object spectroscopy (MOS) at R∼4,000 and ∼40,000 offers a transformational opportunity to characterize stellar activity in the time domain across large samples of exoplanet host stars. Such a facility would enable simultaneous monitoring of continuum variability, chromospheric and coronal line diagnostics, and particle-accelerated flare signatures, resolving the physics driving space weather and quantifying its impact on planetary atmospheres.

Stars and planets can be seen as the second fundamental building blocks of baryons in the universe (only second to the dust and gas in molecular clouds). Their formation involves dust grain growth of many orders of magnitude and a myriad of processes operating at time scales from a few tens to millions of years. Thus, investigating the formation and evolution of young stellar objects (YSOs) is of great importance in modern astronomy. Addressing this goal requires overcoming long-standing challenges in characterizing multifaceted phenomena that span a broad range of astrophysical processes (from protoplanetary disk evolution and planet formation to accretion dynamics and transient stellar events). Also, YSOs are complex systems that consist of several components: a central forming object, surrounded by a medium or disk from which the accretion process is at work, supersonic ejection of plasma in the form of collimated bipolar jets (which interact with the ambient medium through which they propagate) and all these components emit in a wide range of wavelengths. A facility capable of simultaneously tackling these diverse questions must deliver long-term, high-cadence spectroscopic monitoring of YSOs over time spans of at least a decade; especially because accretion/ejection processes in YSOs are characterized by a wide range of temporal variability: from short-term (hours-days) to long-term (months-years) variability due to rotation, accretion, magnetic activity, etc. Such a mission demands a spectroscopic platform considering a solid time-domain astronomy framework, providing repeated observations over wide fields and supporting multiple cadence strategies tailored to distinct scientific objectives.

We present the first large-scale 3D kinematic study of ~2000 spectroscopically-confirmed young stars (<20 Myr) in 18 star clusters and OB associations (hereafter groups) from the combination of Gaia astrometry and Gaia-ESO Survey spectroscopy. We measure 3D velocity dispersions for all groups, which range from 0.61 to 7.4 km/s (1D velocity dispersions of 0.35 to 4.3 km/s). We find the majority of groups have anisotropic velocity dispersions, suggesting they are not dynamically relaxed. From the 3D velocity dispersions, measured radii and estimates of total mass we estimate the virial state and find that all systems are super-virial when only the stellar mass is considered, but that some systems are sub-virial when the mass of the molecular cloud is taken into account. We observe an approximately linear correlation between the 3D velocity dispersion and the group mass, which would imply that the virial state of groups scales as the square root of the group mass. However, we do not observe a strong correlation between virial state and group mass. In agreement with their virial state we find that nearly all of the groups studied are in the process of expanding and that the expansion is anisotropic, implying that groups were not spherical prior to expansion. One group, Rho Oph, is found to be contracting and in a sub-virial state (when the mass of the surrounding molecular cloud is considered). This work provides a glimpse of the potential of the combination of Gaia and data from the next generation of spectroscopic surveys.

Context. The third data release of Gaia, has provided stellar parameters, metallicity [M/H], [alpha/Fe], individual abundances, broadening parameter from its Radial Velocity Spectrograph (RVS) spectra for about 5.6 million objects thanks to the GSP-Spec module, implemented in the Gaia pipeline. The catalogue also publishes the radial velocity of 33 million sources. In recent years, many spectroscopic surveys with ground-based telescopes have been undertaken, including the public survey Gaia-ESO, designed to be complementary to Gaia, in particular towards faint stars. Aims. We took advantage of the intersections between Gaia RVS and Gaia-ESO to compare their stellar parameters, abundances and radial and rotational velocities. We aimed at verifying the overall agreement between the two datasets, considering the various calibrations and the quality-control flag system suggested for the Gaia GSP-Spec parameters. Methods. For the targets in common between Gaia RVS and Gaia-ESO, we performed several statistical checks on the distributions of their stellar parameters, abundances and velocities of targets in common. For the Gaia surface gravity and metallicity we considered both the uncalibrated and calibrated values. Results. Overall, there is a good agreement between the results of the two surveys. We find an excellent agreement between the Gaia and Gaia-ESO radial velocities given the uncertainties affecting each dataset. Less than 25 out of the approximate to 2100 Gaia-ESO spectroscopic binaries are flagged as non-single stars by Gaia. For the effective temperature and in the bright regime (G <= 11), we found a very good agreement, with an absolute residual difference of about 5 K (+/- 90 K) for the giant stars and of about 17 K (+/- 135 K) for the dwarf stars; in the faint regime (G >= 11), we found a worse agreement, with an absolute residual difference of about 107 K (+/- 145 K) for the giant stars and of about 103 K (+/- 258 K) for the dwarf stars. For the surface gravity, the comparison indicates that the calibrated gravity should be preferred to the uncalibrated one. For the metallicity, we observe in both the uncalibrated and calibrated cases a slight trend whereby Gaia overestimates it at low metallicity; for [M/H] and [alpha/Fe], a marginally better agreement is found using the calibrated Gaia results; finally for the individual abundances (Mg, Si, Ca, Ti, S, Cr, Ni, Ce) our comparison suggests to avoid results with flags indicating low quality (XUncer = 2 or higher). These remarks are in line with the ones formulated by GSP-Spec. We confirm that the Gaia vbroad parameter is loosely correlated with the Gaia-ESO v sin i for slow rotators. Finally, we note that the quality (accuracy, precision) of the GSP-Spec parameters degrades quickly for objects fainter than G approximate to 11 or GRVS approximate to 10. Conclusions. We find that the somewhat imprecise GSP-Spec abundances due to its medium-resolution spectroscopy over a short wavelength window and the faint G regime of the sample under study can be counterbalanced by working with averaged quantities. We extended our comparison to star clusters using averaged abundances, using not only the stars in common, but also the members of clusters in common between the two samples, still finding a very good agreement. Encouraged by this result, we studied some properties of the open-cluster population, using both Gaia-ESO and Gaia clusters: our combined sample traces very well the radial metallicity and [Fe/H] gradients, the age-metallicity relations in different radial regions, and allows us to place the clusters in the thin disc.

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate

(abridged) The third data release of Gaia, has provided stellar parameters, metallicity [M/H], [α/Fe], individual abundances, broadening parameter from its RVS spectra for about 5.6 million objects thanks to the GSP-Spec module. The catalogue publishes the radial velocity of 33 million sources. We took advantage of the intersections between Gaia RVS and Gaia-ESO to compare their stellar parameters, abundances and radial and rotational velocities. We aimed at verifying the overall agreement between the two datasets, considering the various calibrations and the quality-control flag system suggested for the Gaia GSP-Spec parameters. For the targets in common between Gaia RVS and Gaia-ESO, we performed several statistical checks on the distributions of their stellar parameters, abundances and velocities of targets in common. For the Gaia surface gravity and metallicity we considered both the uncalibrated and calibrated values. We find an excellent agreement between the Gaia and Gaia-ESO radial velocities given the uncertainties affecting each dataset. Less than 25 of  2100 Gaia-ESO spectroscopic binaries are flagged as non-single stars by Gaia. The temperature scales are in good agreement. The calibrated GSP-Spec gravity should be preferred. We note that the quality (accuracy, precision) of the GSP-Spec parameters degrades quickly for objects fainter than G 11. We find that the somewhat imprecise GSP-Spec abundances due to its medium-resolution spectroscopy over a short wavelength window and the faint G regime of the sample under study can be counterbalanced by working with averaged quantities. We studied some properties of the open-cluster population: our combined sample traces very well the radial [Fe/H] and [Ca/Fe] gradients, the age-metallicity relations in different radial regions, and it places the clusters in the thin disc.

Context. New spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science. Aims. We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. Methods. We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR 10 and HR 21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated. Results. We achieved an accuracy of 28 K in T-eff, 0.06 dex in log g, 0.03 dex in [Fe/H], and between 0.05 dex and 0.17 dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39 K in T-eff, 0.08 dex in log g, and 0.05 dex in [Fe/H]. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. Conclusions. This neural network represents a compelling proposition for future astronomical surveys. These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.

Context.The survival time of a star cluster depends on its total mass, density, and thus size, as well as on the environment in which it was born and in which lies. Its dynamical evolution is influenced by various factors such as gravitational effects of the Galactic bar, spiral structures, and molecular clouds. Overall, the factors that determine the longevity of a cluster are complex and not fully understood.Aims.This study aims to investigate whether open clusters and field stars respond differently to the perturbations that cause radial migration. In particular, we aim to understand the nature of the oldest surviving clusters.Methods.We compared the time evolution of the kinematic properties of twoGaiaDR3 samples. The first sample is composed of ∼40 open clusters and the second one of ∼66 000 main sequence turn off field stars. Both of the samples are composed of stars selected with the same quality criterion, and they belong to the thin disc, are in a similar metallicity range, are located in the same Galactocentric region [7.5–9 kpc], and have ages greater than 1 Gyr. We performed a statistical analysis comparing the properties of the samples of the field stars and of the open clusters.Results.A qualitative comparison of kinematic and orbital properties revealed that clusters younger than 2–3 Gyr are more resistant to perturbations than field stars, and they move along quasi-circular orbits. Conversely, clusters older than approximately 3 Gyr have more eccentric and inclined orbits than isolated stars in the same age range. Such orbits lead the older clusters to reach higher elevations on the Galactic plane, maximising their probability to survive several more gigayears. A formal statistical analysis revealed that there are differences among the time evolution of most of the kinematic and orbital properties of the field stars and open clusters. However, the comparison between some properties (e.g.,VϕandLZ) do not reach a sufficient statistical significance.Conclusions.Our results suggest that the oldest surviving clusters are usually more massive and move on orbits with a higher eccentricity. Although they are still reliable tracers of the Galaxy’s past composition, they do not reflect the composition of the place where they are currently found. Therefore, we cannot avoid considering kinematic properties when comparing data and models of chemical evolution and also taking into account the intrinsic differences between clusters and isolated stars. To validate the results, new studies that increase the sample of open clusters, especially at older ages, are needed.

The High-Resolution Multi-Object Spectrograph (HRMOS) is a facility instrument that we plan to propose for the Very Large Telescope (VLT) of the European Southern Observatory (ESO), following the initial presentation at the VLT 2030 workshop held at ESO in June 2019. HRMOS provides a combination of capabilities that are essential to carry out breakthrough science across a broad range of active research areas from stellar astrophysics and exoplanet studies to Galactic and Local Group archaeology. HRMOS fills a gap in capabilities amongst the landscape of future instrumentation planned for the next decade. The key characteristics of HRMOS will be high spectral resolution (R = 60000 - 80000) combined with multi-object (20-100) capabilities and long term stability that will provide excellent radial velocity precision and accuracy (10m/s). Initial designs predict that a SNR 100 will be achievable in about one hour for a star with mag(AB) = 15, while with the same exposure time a SNR 30 will be reached for a star with mag(AB) = 17. The combination of high resolution and multiplexing with wavelength coverage extending to relatively blue wavelengths (down to 380 nm), makes HRMOS a spectrograph that will push the boundaries of our knowledge and that is envisioned as a workhorse instrument in the future. The science cases presented in this White Paper include topics and ideas developed by the Core Science Team with the contributions from the astronomical community, also through the wide participation in the first HRMOS Workshop (https://indico.ict.inaf.it/event/1547/) that took place in Firenze (Italy) in October 2021.

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.

VISIONS is an ESO public survey of five nearby (d < 500 pc) star-forming molecular cloud complexes that are canonically associated with the constellations of Chamaeleon, Corona Australis, Lupus, Ophiuchus, and Orion. The survey was carried out with VISTA, using VIRCAM, and collected data in the near-infrared passbands J, H, and Ks. With a total on-sky exposure time of 49.4 h VISIONS covers an area of 650 deg$^2$, and it was designed to build an infrared legacy archive similar to that of 2MASS. Taking place between April 2017 and March 2022, the observations yielded approximately 1.15 million images, which comprise 19 TB of raw data. The observations are grouped into three different subsurveys: The wide subsurvey comprises shallow, large-scale observations and has visited the star-forming complexes six times over the course of its execution. The deep subsurvey of dedicated high-sensitivity observations has collected data on the areas with the largest amounts of dust extinction. The control subsurvey includes observations of areas of low-to-negligible dust extinction. Using this strategy, the VISIONS survey offers multi-epoch position measurements, is able to access deeply embedded objects, and provides a baseline for statistical comparisons and sample completeness. In particular, VISIONS is designed to measure the proper motions of point sources with a precision of 1 mas/yr or better, when complemented with data from VHS. Hence, VISIONS can provide proper motions for sources inaccessible to Gaia. VISIONS will enable addressing a range of topics, including the 3D distribution and motion of embedded stars and the nearby interstellar medium, the identification and characterization of young stellar objects, the formation and evolution of embedded stellar clusters and their initial mass function, as well as the characteristics of interstellar dust and the reddening law.

The spatial distribution of elemental abundances and their time evolution are among the major constraints to disentangle the scenarios of formation and evolution of the Galaxy. We used the sample of open clusters available in the final release of the Gaia-ESO survey to trace the Galactic radial abundance and abundance to iron ratio gradients, and their time evolution. We selected member stars in 62 open clusters, with ages from 0.1 to about 7~Gyr, located in the Galactic thin disc at Galactocentric radii from about 6 to 21~kpc. We analysed the shape of the resulting [Fe/H] gradient, the average gradients [El/H] and [El/Fe] combining elements belonging to four different nucleosynthesis channels, and their individual abundance and abundance ratio gradients. We also investigated the time evolution of the gradients dividing open clusters in three age bins. The[Fe/H] gradient has a slope of -0.054 dex~kpc-1. We saw different behaviours for elements belonging to different channels. We found that the youngest clusters in the inner disc have lower metallicity than their older counterpart and they outline a flatter gradient. We considered some possible explanations, including the effects of gas inflow and migration. We suggested that it might be a bias introduced by the standard spectroscopic analysis producing lower metallicities in low gravity stars. To delineate the shape of the `true' gradient, we should limit our analysis to stars with low surface gravity logg>2.5 and xi<1.8 km~s-1. Based on this reduced sample, we can conclude that the gradient has minimally evolved over the time-frame outlined by the open clusters, indicating a slow and stationary formation of the thin disc in the latest Gyr. We found a secondary role of clusters' migration in shaping the gradient, with a more prominent role of migration for the oldest clusters.

The Gaia-ESO Survey is a public spectroscopic survey that targeted ≳105 stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public 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 (Teff ~ 30 000–52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. 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, for example, 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, each specialised in one or more of 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 in order to arrive at a single set of recommended results for use by all survey collaborators. We also present some general quality and consistency checks performed on the entirety of the survey results.

ABSTRACT 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 to 6000 Myr and −0.3 < [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 6708 Å equivalent width for individual (pre) main-sequence stars with 3000 < Teff/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 2. 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 < [Fe/H] < 0.18. Our methods and model are provided as software – ‘Empirical AGes from Lithium Equivalent widthS’ (eagles).

Context. To take full advantage of upcoming large-scale spectroscopic surveys, it will be necessary to parameterize millions of stellar spectra in an efficient way. Machine learning methods, especially convolutional neural networks (CNNs), will be among the main tools geared at achieving this task. Aims. We aim to prepare the groundwork for machine learning techniques for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that CNNs can predict accurate stellar labels from relevant spectral features in a physically meaningful way. The predicted labels can be used to investigate properties of the Milky Way galaxy. Methods. We built a neural network and trained it on GIRAFFE spectra with their associated stellar labels from the sixth internal Gaia-ESO data release. Our network architecture contains several convolutional layers that allow the network to identify absorption features in the input spectra. The internal uncertainty was estimated from multiple network models. We used the t-distributed stochastic neighbor embedding tool to remove bad spectra from our training sample. Results. Our neural network is able to predict the atmospheric parameters T-eff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe], and [Fe/H] for 36 904 stellar spectra. The training precision is 37 K for T-eff, 0.06 dex for log(g), 0.05 dex for [Mg/Fe], 0.08 dex for [Al/Fe], and 0.04 dex for [Fe/H]. Network gradients reveal that the network is inferring the labels in a physically meaningful way from spectral features. We validated our methodology using benchmark stars and recovered the properties of different stellar populations in the Milky Way galaxy. Conclusions. Such a study provides very good insights into the application of machine learning for the analysis of large-scale spectroscopic surveys, such as WEAVE and 4MOST Milky Way disk and bulge low- and high-resolution (4MIDABLE-LR and -HR). The community will have to put substantial efforts into building proactive training sets for machine learning methods to minimize any possible systematics.

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution.

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.

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.

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.