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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. 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]