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- 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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| 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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| 5. |
- Tautvaišiene, G., et al.
(författare)
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Gaia -ESO Survey : Detailed elemental abundances in red giants of the peculiar globular cluster NGC 1851
- 2022
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 658
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Tidskriftsartikel (refereegranskat)abstract
- Context. NGC 1851 is one of several globular clusters for which multiple stellar populations of the subgiant branch have been clearly identified and a difference in metallicity detected. A crucial piece of information on the formation history of this cluster can be provided by the sum of A(C+N+O) abundances. However, these values have lacked a general consensus thus far. The separation of the subgiant branch can be based on age and/or A(C+N+O) abundance differences. Aims. Our main aim was to determine carbon, nitrogen, and oxygen abundances for evolved giants in the globular cluster NGC 1851 in order to check whether or not the double populations of stars are coeval. Methods. High-resolution spectra, observed with the FLAMES-UVES spectrograph on the ESO VLT telescope, were analysed using a differential model atmosphere method. Abundances of carbon were derived using spectral synthesis of the C2 band heads at 5135 and 5635.5 Å. The wavelength interval 6470-6490 Å, with CN features, was analysed to determine nitrogen abundances. Oxygen abundances were determined from the [O I] line at 6300 Å. Abundances of other chemical elements were determined from equivalent widths or spectral syntheses of unblended spectral lines. Results. We provide abundances of up to 29 chemical elements for a sample of 45 giants in NGC 1851. The investigated stars can be separated into two populations with a difference of 0.07 dex in the mean metallicity, 0.3 dex in the mean C/N, and 0.35 dex in the mean s-process dominated element-to-iron abundance ratios [s/Fe]. No significant difference was determined in the mean values of A(C+N+O) as well as in abundance to iron ratios of carbon, α- and iron-peak-elements, and of europium. Conclusions. As the averaged A(C+N+O) values between the two populations do not differ, additional evidence is given that NGC 1851 is composed of two clusters, the metal-rich cluster being by about 0.6 Gyr older than the metal-poor one. A global overview of NGC 1851 properties and the detailed abundances of chemical elements favour its formation in a dwarf spheroidal galaxy that was accreted by the Milky Way.
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