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- Guiglion, G., et al.
(author)
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Beyond Gaia DR3 : Tracing the [α/M] - [M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks
- 2024
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In: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 682
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Journal article (peer-reviewed)abstract
- 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.
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| 2. |
- Worley, C. C., et al.
(author)
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The Gaia-ESO Survey : The DR5 analysis of the medium-resolution GIRAFFE and high-resolution UVES spectra of FGK-type stars
- 2024
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In: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 684
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Journal article (peer-reviewed)abstract
- The Gaia-ESO Survey is an European Southern Observatory (ESO) public spectroscopic survey that targeted 105 stars in the Milky Way covering the major populations of the disk, bulge and halo. The observations were made using FLAMES on the VLT obtaining both UVES high (R ~ 47 000) and GIRAFFE medium (R ~ 20 000) resolution spectra. The analysis of the Gaia-ESO spectra was the work of multiple analysis teams (nodes) within five working groups (WG). The homogenisation of the stellar parameters within WG11 (high resolution observations of FGK stars) and the homogenisation of the stellar parameters within WG10 (medium resolution observations of FGK stars) is described here. In both cases, the homogenisation was carried out using a Bayesian Inference method developed specifically for the Gaia-ESO Survey by WG11. The method was also used for the chemical abundance homogenisation within WG11, however, the WG10 chemical abundance data set was too sparsely populated so basic corrections for each node analysis were employed for the homogenisation instead. The WG10 homogenisation primarily used the cross-match of stars with WG11 as the reference set in both the stellar parameter and chemical abundance homogenisation. In this way the WG10 homogenised results have been placed directly onto the WG11 stellar parameter and chemical abundance scales. The reference set for the metal-poor end was sparse which limited the effectiveness of the homogenisation in that regime. For WG11, the total number of stars for which stellar parameters were derived was 6 231 with typical uncertainties for Teff, log g and [Fe/H] of 32 K, 0.05 and 0.05 respectively. One or more chemical abundances out of a possible 39 elements were derived for 6 188 of the stars. For WG10, the total number of stars for which stellar parameters were derived was 76 675 with typical uncertainties for Teff, log g and [Fe/H] of 64 K, 0.15 and 0.07 respectively. One or more chemical abundances out of a possible 30 elements were derived for 64177 of the stars.
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