| 1. |
- Hourihane, A., et al.
(författare)
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The Gaia-ESO Survey : Homogenisation of stellar parameters and elemental abundances
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 676
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Tidskriftsartikel (refereegranskat)abstract
- The Gaia-ESO Survey is a public spectroscopic survey that targeted greater than or similar to 10(5) 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 (T-eff similar to 30 000-52 000 K) all the way to K-M (greater than or similar to 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.
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| 2. |
- Ambrosch, M., et al.
(författare)
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The Gaia -ESO Survey : Chemical evolution of Mg and Al in the Milky Way with machine learning
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 672
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Tidskriftsartikel (refereegranskat)abstract
- 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 Teff 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 Teff, 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.
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| 3. |
- Dantas, M. L.L., et al.
(författare)
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The Gaia -ESO Survey : Old super-metal-rich visitors from the inner Galaxy
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 669
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Tidskriftsartikel (refereegranskat)abstract
- Context. The solar vicinity is currently populated by a mix of stars with various chemo-dynamic properties, including stars with a high metallicity compared to the Sun. Dynamical processes such as churning and blurring are expected to relocate such metal-rich stars from the inner Galaxy to the solar region. Aims. We report the identification of a set of old super-metal-rich (+0.15 ≤ [Fe/H] ≤ +0.50) dwarf stars with low eccentricity orbits (e ≤ 0.2) that reach a maximum height from the Galactic plane in the range ≤0.5-1.5 kpc. We discuss their chemo-dynamic properties with the goal of understanding their potential origins. Methods. We used 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 (i.e. 21 dimensions). We applied a hierarchical clustering algorithm to group the stars with similar chemical abundances within the complete chemical abundance space. Orbits were integrated using astrometric data from Gaia and radial velocities from Gaia-ESO. Stellar ages were estimated using isochrones and a Bayesian method. Results. This set of super-metal-rich stars can be arranged into five subgroups, according to their chemical properties. Four of these groups seem to follow a chemical enrichment flow, where nearly all abundances increase in lockstep with Fe. The fifth subgroup shows different chemical characteristics. All the subgroups have the following features: median ages of the order of 7-9 Gyr (with five outlier stars of estimated younger age), solar or subsolar [Mg/Fe] ratios, maximum height from the Galactic plane in the range 0.5-1.5 kpc, low eccentricities (e ≤ 0.2), and a detachment from the expected metallicity gradient with guiding radius (which varies between ~6 and 9 kpc for the majority of the stars). Conclusions. 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, more importantly, to churning. We therefore suggest that most of the stars in this population originated in the inner regions of the Milky Way (inner disc and/or the bulge) and later migrated to the solar neighbourhood. The region where the stars originated had a complex chemical enrichment history, with contributions from supernovae types Ia and II, and possibly asymptotic giant branch stars as well.
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| 4. |
- Nepal, S., et al.
(författare)
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The Gaia-ESO Survey : Preparing the ground for 4MOST and WEAVE galactic surveys: Chemical evolution of lithium with machine learning
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 671
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Tidskriftsartikel (refereegranskat)abstract
- 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 A 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.
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