Search

Context. In June 2022, Gaia DR3 provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200 million spectra. Thus, stellar spectra are projected to be produced on an 'industrial scale', with numbers well above those for current and anticipated ground-based surveys. 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. Aims. 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. Methods. 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. Results. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886 080 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. Conclusions. 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. [ABSTRACT FROM AUTHOR]

Context. Abundances of slow neutron-capture process (s-process) elements in stars with exquisite asteroseismic, spectroscopic, and astrometric constraints offer a novel opportunity to study stellar evolution, nucleosynthesis, and Galactic chemical evolution. Aims. We investigate one of the least studied s-process elements in the literature, cerium (Ce), using stars with asteroseismic constraints from the Kepler, K2, and TESS missions. Methods. We combined the global asteroseismic parameters derived from precise light curves obtained by the Kepler, K2, and TESS missions with stellar parameters and chemical abundances from the latest data release of the large spectroscopic survey APOGEE and astrometric data from the Gaia mission. Finally, we computed stellar ages using the code PARAM with a Bayesian estimation method. Results. We investigated the different trends of [Ce/Fe] as a function of metallicity, [α/Fe], and age taking into account the dependence on the radial position, especially in the case of K2 targets, which cover a wide galactocentric range. We finally explored the [Ce/α] ratios as a function of age in different galactocentric intervals. Conclusions The studied trends display a strong dependence of the Ce abundances on the metallicity and star formation history. The [Ce/Fe] ratio shows a non-monotonic dependence on [Fe/H] with a peak around −0.2 dex. Moreover, younger stars have higher [Ce/Fe] and [Ce/α] ratios than older stars, confirming the latest contribution of low- and intermediate-mass asymptotic giant branch stars to the Galactic chemical enrichment. In addition, the trends of [Ce/Fe] and [Ce/α] with age become steeper moving towards the outer regions of the Galactic disc, demonstrating more intense star formation in the inner regions than in the outer regions. Cerium is thus a potentially interesting element to help constrain stellar yields and the inside-out formation of the Milky Way disc. However, the large scatter in all the relations studied here suggests that spectroscopic uncertainties for this element are still too large. [ABSTRACT FROM AUTHOR]

Context. To take full advantage of upcoming large-scale spectroscopic surveys, it will be necessary to parameterize millions of stellar spectra in an effcient 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 Te 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 37K for Te, 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 MilkyWay 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. [ABSTRACT FROM AUTHOR]

The information on Galactic assembly time is imprinted on the chemodynamics of globular clusters. This makes them important probes that help us to understand the formation and evolution of the Milky Way. Discerning between in-situ and ex-situ origin of these objects is difficult when we study the Galactic bulge, which is the most complex and mixed component of the Milky Way. To investigate the early evolution of the Galactic bulge, we analysed the globular cluster NGC 6355. We derived chemical abundances and kinematic and dynamic properties by gathering information from high-resolution spectroscopy with FLAMES-UVES, photometry with the Hubble Space Telescope, and Galactic dynamic calculations applied to the globular cluster NGC 6355. We derive an age of 13.2 ± 1.1 Gyr and a metallicity of [Fe/H]  =   − 1.39 ± 0.08 for NGC 6355, with α-enhancement of [α/Fe]  =   + 0.37 ± 0.11. The abundance pattern of the globular cluster is compatible with bulge field RR Lyrae stars and in-situ well-studied globular clusters. The orbital parameters suggest that the cluster is currently confined within the bulge volume when we consider a heliocentric distance of 8.54 ± 0.19 kpc and an extinction coefficient of RV = 2.84 ± 0.02. NGC 6355 is highly likely to come from the main bulge progenitor. Nevertheless, it still has a low probability of being formed from an accreted event because its age is uncertain and because of the combined [Mg/Mn] [Al/Fe] abundance. Its relatively low metallicity with respect to old and moderately metal-poor inner Galaxy clusters may suggest a low-metallicity floor for globular clusters that formed in-situ in the early Galactic bulge. [ABSTRACT FROM AUTHOR]

Context. 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. Aims. 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. Methods. 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 atmospheric parameters and lithium abundances for ∼40 000 stars. The CNN architecture and accompanying notebooks are available online via GitHub. Results. 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 lithium feature at 6707.8 Å is successfully singled out by our CNN, among the thousands of lines in the GIRAFFE HR15N setup. Rare objects such as lithium-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample. Conclusions. 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 machine-learning applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances. [ABSTRACT FROM AUTHOR]

We investigate the inner regions of the Milky Way using data from APOGEE and Gaia EDR3. Our inner Galactic sample has more than 26 500 stars within |XGal|< 5 kpc, |YGal|< 3.5 kpc, |ZGal|< 1 kpc, and we also carry out the analysis for a foreground-cleaned subsample of 8000 stars that is more representative of the bulge–bar populations. These samples allow us to build chemo-dynamical maps of the stellar populations with vastly improved detail. The inner Galaxy shows an apparent chemical bimodality in key abundance ratios [α/Fe], [C/N], and [Mn/O], which probe different enrichment timescales, suggesting a star formation gap (quenching) between the high- and low-α populations. Using a joint analysis of the distributions of kinematics, metallicities, mean orbital radius, and chemical abundances, we can characterize the different populations coexisting in the innermost regions of the Galaxy for the first time. The chemo-kinematic data dissected on an eccentricity–|Z|max plane reveal the chemical and kinematic signatures of the bar, the thin inner disc, and an inner thick disc, and a broad metallicity population with large velocity dispersion indicative of a pressure-supported component. The interplay between these different populations is mapped onto the different metallicity distributions seen in the eccentricity–|Z|max diagram consistently with the mean orbital radius and Vϕ distributions. A clear metallicity gradient as a function of |Z|max is also found, which is consistent with the spatial overlapping of different populations. Additionally, we find and chemically and kinematically characterize a group of counter-rotating stars that could be the result of a gas-rich merger event or just the result of clumpy star formation during the earliest phases of the early disc that migrated into the bulge. Finally, based on 6D information, we assign stars a probability value of being on a bar orbit and find that most of the stars with large bar orbit probabilities come from the innermost 3 kpc, with a broad dispersion of metallicity. Even stars with a high probability of belonging to the bar show chemical bimodality in the [α/Fe] versus [Fe/H] diagram. This suggests bar trapping to be an efficient mechanism, explaining why stars on bar orbits do not show a significant, distinct chemical abundance ratio signature. [ABSTRACT FROM AUTHOR]

Context. Data-driven methods play an increasingly important role in the field of astrophysics. In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). Aims. We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. Methods. We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R = 22 500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R ∼ 7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. Results. We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K in Teff, 0.06 in log(g) and 0.02−0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey. Conclusions. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys. [ABSTRACT FROM AUTHOR]

The Galactic center region, including the nuclear disk, has until recently been largely avoided in chemical census studies because of extreme extinction and stellar crowding. Large, near-IR spectroscopic surveys, such as the Apache Point Observatory Galactic Evolution Experiment (APOGEE), allow the measurement of metallicities in the inner region of our Galaxy. Making use of the latest APOGEE data release (DR16), we are able for the first time to study cool Asymptotic Giant branch (AGB) stars and supergiants in this region. The stellar parameters of five known AGB stars and one supergiant star (VR 5-7) show that their location is well above the tip of the red giant branch. We studied metallicities of 157 M giants situated within 150 pc of the Galactic center from observations obtained by the APOGEE survey with reliable stellar parameters from the APOGEE pipeline making use of the cool star grid down to 3200 K. Distances, interstellar extinction values, and radial velocities were checked to confirm that these stars are indeed situated in the Galactic center region. We detect a clear bimodal structure in the metallicity distribution function, with a dominant metal-rich peak of [Fe/H] ∼ +0.3 dex and a metal-poor peak around {Fe/H] = −0.5 dex, which is 0.2 dex poorer than Baade's Window. The α-elements Mg, Si, Ca, and O show a similar trend to the Galactic bulge. The metal-poor component is enhanced in the α-elements, suggesting that this population could be associated with the classical bulge and a fast formation scenario. We find a clear signature of a rotating nuclear stellar disk and a significant fraction of high-velocity stars with vgal >  300 km s−1; the metal-rich stars show a much higher rotation velocity (∼200 km s−1) with respect to the metal-poor stars (∼140 km s−1). The chemical abundances as well as the metallicity distribution function suggest that the nuclear stellar disk and the nuclear star cluster show distinct chemical signatures and might be formed differently. [ABSTRACT FROM AUTHOR]

Context. Several new multi-object spectrographs are currently planned or under construction that are capable of observing thousands of Galactic and extragalactic objects simultaneously. Aims. In this paper we present a probabilistic fibre-to-target assignment algorithm that takes spectrograph targeting constraints into account and is capable of dealing with multiple concurrent surveys. We present this algorithm using the 4-m Multi-Object Spectroscopic Telescope (4MOST) as an example. Methods. The key idea of the proposed algorithm is to assign probabilities to fibre-target pairs. The assignment of probabilities takes the fibre positioner's capabilities and constraints into account. Additionally, these probabilities include requirements from surveys and take the required exposure time, number density variation, and angular clustering of targets across each survey into account. The main advantage of a probabilistic approach is that it allows for accurate and easy computation of the target selection function for the different surveys, which involves determining the probability of observing a target, given an input catalogue. Results. The probabilistic fibre-to-target assignment allows us to achieve maximally uniform completeness within a single field of view. The proposed algorithm maximises the fraction of successfully observed targets whilst minimising the selection bias as a function of exposure time. In the case of several concurrent surveys, the algorithm maximally satisfies the scientific requirements of each survey and no specific survey is penalised or prioritised. Conclusions. The algorithm presented is a proposed solution for the 4MOST project that allows for an unbiased targeting of many simultaneous surveys. With some modifications, the algorithm may also be applied to other multi-object spectroscopic surveys. [ABSTRACT FROM AUTHOR]

Context. Very metal-poor halo stars are the best candidates for being among the oldest objects in our Galaxy. Samples of halo stars with age determination and detailed chemical composition measurements provide key information for constraining the nature of the first stellar generations and the nucleosynthesis in the metal-poor regime. Aims. Age estimates are very uncertain and are available for only a small number of metal-poor stars. We present the first results of a pilot programme aimed at deriving precise masses, ages, and chemical abundances for metal-poor halo giants using asteroseismology and high-resolution spectroscopy. Methods. We obtained high-resolution UVES spectra for four metal-poor RAVE stars observed by the K2 satellite. Seismic data obtained from K2 light curves helped improve spectroscopic temperatures, metallicities, and individual chemical abundances. Mass and ages were derived using the code PARAM, investigating the effects of different assumptions (e.g. mass loss and [α/Fe]-enhancement). Orbits were computed using Gaia DR2 data. Results. The stars are found to be normal metal-poor halo stars (i.e. non C-enhanced), and an abundance pattern typical of old stars (i.e. α and Eu-enhanced), and have masses in the 0.80−1.0 M⊙ range. The inferred model-dependent stellar ages are found to range from 7.4 Gyr to 13.0 Gyr with uncertainties of ∼30%−35%. We also provide revised masses and ages for metal-poor stars with Kepler seismic data from the APOGEE survey and a set of M4 stars. Conclusions. The present work shows that the combination of asteroseismology and high-resolution spectroscopy provides precise ages in the metal-poor regime. Most of the stars analysed in the present work (covering the metallicity range of [Fe/H] ∼ −0.8 to −2 dex) are very old >9 Gyr (14 out of 19 stars), and all of the stars are older than >5 Gyr (within the 68 percentile confidence level). [ABSTRACT FROM AUTHOR]

We propose here that the lithium decrease at super-solar metallicities observed in high-resolution spectroscopic surveys can be explained by the interplay of mixed populations that originate in the inner regions of the Milky Way disk. The lower lithium content of these stars is a consequence of inside-out disk formation plus radial migration. In this framework, local stars with super-solar metallicities would have migrated to the solar vicinity and depleted their original lithium during their travel time. To obtain this result, we took advantage of the AMBRE catalog of lithium abundances combined with chemical evolution models that take into account the contribution to the lithium enrichment by different nucleosynthetic sources. A large proportion of migrated stars can explain the observed lower lithium abundance at super-solar metallicities. We stress that no stellar model is currently able to predict Li-depletion for these super-solar metallicity stars, and solar Li-depletion has to be assumed. In addition, no solid quantitative estimate of the proportion of migrated stars in the solar neighborhood and their travel time is currently available. Our results illustrate how important it is to properly include radial migration when chemical evolution models are compared to observations, and that in this case, the lithium decrease at larger metallicities does not necessarily imply that stellar yields have to be modified, contrary to previous claims in the literature. [ABSTRACT FROM AUTHOR]

In the era of large-scale Galactic astronomy and multi-object spectroscopic stellar surveys, the sample sizes and the number of available stellar chemical abundances have reached dimensions in which it has become difficult to process all the available information in an effective manner. In this paper we demonstrate the use of a dimensionality-reduction technique (t-distributed stochastic neighbour embedding; t-SNE) for analysing the stellar abundance-space distribution. While the non-parametric non-linear behaviour of this technique makes it difficult to estimate the significance of any abundance-space substructure found, we show that our results depend little on parameter choices and are robust to abundance errors. By reanalysing the high-resolution high-signal-to-noise solar-neighbourhood HARPS-GTO sample with t-SNE, we find clearer chemical separations of the high- and low-[α/Fe] disc sequences, hints for multiple populations in the high-[α/Fe] population, and indications that the chemical evolution of the high-[α/Fe] metal-rich stars is connected with the super-metal-rich stars. We also identify a number of chemically peculiar stars, among them a high-confidence s-process-enhanced abundance-ratio pair (HD 91345/HD 126681) with very similar ages and vX and vY velocities, which we suggest have a common birth origin, possibly a dwarf galaxy. Our results demonstrate the potential of abundance-space t-SNE and similar methods for chemical-tagging studies with large spectroscopic surveys. [ABSTRACT FROM AUTHOR]

We present a set of 87 RAVE stars with detected solar like oscillations, observed during Campaign 1 of the K2 mission (RAVE K2-C1 sample). This data set provides a useful benchmark for testing the gravities provided in RAVE data release 4 (DR4), and is key for the calibration of the RAVE data release 5 (DR5). The RAVE survey collected medium-resolution spectra (R = 7500) centred in the Ca II triplet(8600 Å) wavelength interval, which although being very useful for determining radial velocity and metallicity, even at low S/N, is known be affected by a log (g)-Teff degeneracy. This degeneracy is the cause of the large spread in the RAVE DR4 gravities for giants. The understanding of the trends and offsets that affects RAVE atmospheric parameters, and in particular log (g), is a crucial step in obtaining not only improved abundance measurements, but also improved distances and ages. In the present work, we use two different pipelines, GAUFRE and Sp_Ace, to determine atmospheric parameters and abundances by fixing log (g) to the seismic one. Our strategy ensures highly consistent values among all stellar parameters, leading to more accurate chemical abundances. A comparison of the chemical abundances obtained here with and without the use of seismic log(g) information has shown that an underestimated (overestimated) gravity leads to an underestimated (overestimated) elemental abundance (e.g. [Mg/H] is underestimated by ~0.25 dex when the gravity is underestimated by 0.5 dex). We then perform a comparison between the seismic gravities and the spectroscopic gravities presented in the RAVE DR4 catalogue, extracting a calibration for log(g) of RAVE giants in the colour interval 0.50 < (J-KS) < 0.85. Finally, we show a comparison of the distances, temperatures, extinctions (and ages) derived here for our RAVE K2-C1 sample with those derived in RAVE DR4 and DR5. DR5 performs better than DR4 thanks to the seismic calibration, although discrepancies can still be important for objects for which the difference between DR4/DR5 and seismic gravities differ by more than ~0.5 dex. The method illustrated in this work will be used for analysing RAVE targets present in the other K2 campaigns, in the framework of Galactic Archaeology investigations. [ABSTRACT FROM AUTHOR]

Using combined asteroseismic and spectroscopic observations of 418 red-giant stars close to the Galactic disc plane (6 kpc < RGal≤ 13 kpc, &#124ZGal|< 0:3 kpc), we measure the age dependence of the radial metallicity distribution in the Milky Way's thin disc over cosmic time. The slope of the radial iron gradient of the young red-giant population (-0.058 ± 0.008 [stat.] ±0.003 [syst.] dex/kpc) is consistent with recent Cepheid measurements. For stellar populations with ages of 1-4 Gyr the gradient is slightly steeper, at a value of -0.066 ± 0.007 ± 0.002 dex/kpc, and then flattens again to reach a value of 0:03 dex/kpc for stars with ages between 6 and 10 Gyr. Our results are in good agreement with a state-of-the-art chemo-dynamical Milky-Way model in which the evolution of the abundance gradient and its scatter can be entirely explained by a non-varying negative metallicity gradient in the interstellar medium, together with stellar radial heating and migration. We also offer an explanation for why intermediate-age open clusters in the solar neighbourhood can be more metal-rich, and why their radial metallicity gradient seems to be much steeper than that of the youngest clusters. Already within 2 Gyr, radial mixing can bring metal-rich clusters from the innermost regions of the disc to Galactocentric radii of 5 to 8 kpc. We suggest that these outward-migrating clusters may be less prone to tidal disruption and therefore steepen the local intermediate-age cluster metallicity gradient. Our scenario also explains why the strong steepening of the local iron gradient with age is not seen in field stars. In the near future, asteroseismic data from the K2 mission will allow for improved statistics and a better coverage of the inner-disc regions, thereby providing tighter constraints on the evolution of the central parts of the Milky Way. [ABSTRACT FROM AUTHOR]

With the advent of the space missions CoRoT and Kepler, it has recently become feasible to determine precise asteroseismic masses and relative ages for large samples of red giant stars. We present the CoRoGEE dataset, obtained from CoRoT light curves for 606 red giants in two fields of the Galactic disc that have been co-observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We used the Bayesian parameter estimation code PARAM to calculate distances, extinctions, masses, and ages for these stars in a homogeneous analysis, resulting in relative statistical uncertainties of .2% in distance, ~4% in radius, ~9% in mass and ~25% in age. We also assessed systematic age uncertainties stemming from different input physics and mass loss. We discuss the correlation between ages and chemical abundance patterns of field stars over a broad radial range of the Milky Way disc (5 kpc < RGal < 14 kpc), focussing on the [ /Fe]-[Fe/H]-age plane in five radial bins of the Galactic disc. We find an overall agreement with the expectations of pure chemical-evolution models computed before the present data were available, especially for the outer regions. However, our data also indicate that a significant fraction of stars now observed near and beyond the solar neighbourhood migrated from inner regions. Mock CoRoGEE observations of a chemodynamical Milky Way disc model indicate that the number of high-metallicity stars in the outer disc is too high to be accounted for even by the strong radial mixing present in the model. The mock observations also show that the age distribution of the [ /Fe]-enhanced sequence in the CoRoGEE inner-disc field is much broader than expected from a combination of radial mixing and observational errors. We suggest that a thick-disc/bulge component that formed stars for more than 3 Gyr may account for these discrepancies. Our results are subject to future improvements due to (a) the still low statistics, because our sample had to be sliced into bins of Galactocentric distances and ages; (b) large uncertainties in proper motions (and therefore guiding radii); and (c) corrections to the asteroseismic mass-scaling relation. The situation will improve not only upon the upcoming Gaia data releases, but also with the foreseen increase in the number of stars with both seismic and spectroscopic information. [ABSTRACT FROM AUTHOR]

Context. The dominant astrophysical production site of the r-process elements has not yet been unambiguously identified. The suggested main r-process sites are core-collapse supernovae and merging neutron stars. Aims. We explore the problem of the production site of Eu. We also use the information present in the observed spread in the Eu abundances in the early Galaxy, and not only its average trend. Moreover, we extend our investigations to other heavy elements (Ba, Sr, Rb, Zr) to provide additional constraints on our results. Methods. We adopt a stochastic chemical evolution model that takes inhomogeneous mixing into account. The adopted yields of Eu from merging neutron stars and from core-collapse supernovae are those that are able to explain the average [Eu/Fe]-[Fe/H] trend observed for solar neighbourhood stars, the solar abundance of Eu, and the present-day abundance gradient of Eu along the Galactic disc in the framework of a well-tested homogeneous model for the chemical evolution of the Milky Way. Rb, Sr, Zr, and Ba are produced by both the s- and r-processes. The r-process yields were obtained by scaling the Eu yields described above according to the abundance ratios observed in r-process rich stars. The s-process contribution by spinstars is the same as in our previous papers. Results. Neutron star binaries that merge in less than 10 Myr or neutron star mergers combined with a source of r-process generated by massive stars can explain the spread of [Eu/Fe] in the Galactic halo. The combination of r-process production by neutron star mergers and s-process production by spinstars is able to reproduce the available observational data for Sr, Zr, and Ba. We also show the first predictions for Rb in the Galactic halo. Conclusions. We confirm previous results that either neutron star mergers on a very short timescale or both neutron star mergers and at least a fraction of Type II supernovae have contributed to the synthesis of Eu in the Galaxy. The r-process production of Sr, Zr, and Ba by neutron star mergers - complemented by an s-process production by spinstars - provide results that are compatible with our previous findings based on other r-process sites. We critically discuss the weak and strong points of both neutron star merging and supernova scenarios for producing Eu and eventually suggest that the best solution is probably a mixed one in which both sources produce Eu. In fact, this scenario reproduces the scatter observed in all the studied elements better. [ABSTRACT FROM AUTHOR]

In the first paper of this series (Paper I) we presented a new approach for studying the chemodynamical evolution in disk galaxies, focusing on the Milky Way. While in Paper I we studied extensively the Solar vicinity, here we extend these results to different distances from the Galactic center, looking for variations of observables that can be related to on-going and future spectroscopic surveys. By separating the effects of kinematic heating and radial migration, we show that migration is much more important, even for the oldest and hottest stellar population. The distributions of stellar birth guiding radii and final guiding radii (signifying contamination from migration and heating, respectively) widen with increasing distance from the Galactic center. As a result, the slope in the agemetallicity relation flattens significantly at Galactic radii larger than solar. We predict that the metallicity distributions of (unbiased) samples at different distances from the Galactic center peak at approximately the same value, [Fe/H] ≈ -0.15 dex, and have similar metal-poor tails extending to [Fe/H] ≈ -1.3 dex. In contrast, the metal-rich tail decreases with increasing radius, thus giving rise to the expected decline of mean metallicity with radius. Similarly, the [Mg/Fe] distribution always peaks at ≈0.15 dex, but its low-end tail is lost as radius increases, while the high-end diminishes at [Mg/Fe] ≈ 0.45 dex. The radial metallicity and [Mg/Fe] gradients in our model show significant variations with height above the plane because of changes in the mixture of stellar ages. An inversion in the radial metallicity gradient is found from negative to weakly positive (at r < 10 kpc), and from positive to negative for the [Mg/Fe] gradient, with increasing distance from the disk plane. We relate this to the combined effect of (i) the predominance of young stars close to the disk plane and old stars away from it; (ii) the more concentrated older stellar component; and (iii) the flaring of mono-age disk populations. We also investigate the effect of recycled gas flows on the mean [Fe/H] and find that in the region 4 < r < 12 kpc the introduced errors are less than 0.05-0.1 dex, related to the fact that inward and outward flows mostly cancel in that radial range. We show that radial migration cannot compete with the inside-out formation of the disk, exposed by the more centrally concentrated older disk populations, and consistent with recent observations. [ABSTRACT FROM AUTHOR]

Context. The [Sr/Ba] and [Y/Ba] scatter observed in some galactic halo stars that are very metal-poor and in a few individual stars of the oldest known Milky Way globular cluster NGC 6522 have been interpreted as evidence of early enrichment by massive fastrotating stars (spinstars). Because NGC 6522 is a bulge globular cluster, the suggestion was that not only the very-metal poor halo stars, but also bulge stars at [Fe/H] ∼-1 could be used as probes of the stellar nucleosynthesis signatures from the earlier generations of massive stars, but at much higher metallicity. For the bulge the suggestions were based on early spectra available for stars in NGC 6522, with a medium resolution of R ∼ 22 000 and a moderate signal-to-noise ratio. Aims. The main purpose of this study is to re-analyse the NGC 6522 stars reported previously by using new high-resolution (R ∼ 45 000) and high signal-to-noise spectra (S /N > 100).We aim at re-deriving their stellar parameters and elemental ratios, in particular the abundances of the neutron-capture s-process-dominated elements such as Sr, Y, Zr, La, and Ba, and of the r-element Eu. Methods. High-resolution spectra of four giants belonging to the bulge globular cluster NGC 6522 were obtained at the 8m VLT UT2-Kueyen telescope with the UVES spectrograph in FLAMES-UVES configuration. The spectroscopic parameters were derived based on the excitation and ionization equilibrium of Fe i and Fe ii. Results. Our analysis confirms a metallicity [Fe/H] = -0.95 ± 0.15 for NGC 6522 and the overabundance of the studied stars in Eu (with +0.2 < [Eu/Fe] < +0.4) and alpha-elements O and Mg. The neutron-capture s-element-dominated Sr, Y, Zr, Ba, and La now show less pronounced variations from star to star. Enhancements are in the range 0.0 < [Sr/Fe] < +0.4, +0.23 < [Y/Fe] < +0.43, 0.0 < [Zr/Fe] < +0.4, 0.0 < [La/Fe] < +0.35, and 0.05 < [Ba/Fe] < +0.55. Conclusions. The very high overabundances of [Y/Fe] previously reported for the four studied stars is not confirmed with the new high-quality spectra. The moderate enhancement in [Sr/Fe] previously reported for one of the re-studied stars is confirmed, but the strong enhancements of this ratio for the other two stars are not confirmed. Despite the moderate enhancements found for the neutroncapture s-element-dominated species, none of the four stars studied here show positive values for all [Sr/Ba], [Y/Ba] and [Zr/Ba] ratios. The re-studied stars are now compatible not only with the interpretation that the s-process enhancements in these very old stars are due to spinstars, but also with alternative models such as mass transfer from s-process-rich AGB stars. Note, however, that when our results are interpreted in the context of more extended datasets from the literature, the spinstar scenario still seems to be favoured. [ABSTRACT FROM AUTHOR]