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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. |
- Pagnini, G., et al.
(author)
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The distribution of globular clusters in kinematic spaces does not trace the accretion history of the host galaxy
- 2023
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 673
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Journal article (peer-reviewed)abstract
- Context. Reconstructing how all the stellar components of the Galaxy formed and assembled over time by studying the properties of the stars that form it is the aim of Galactic archaeology. Thanks to the launch of the ESA Gaia astrometric mission and the development of many spectroscopic surveys in recent years, we are for the first time in the position to delve into the layers of the past of the Galaxy. Globular clusters play a fundamental role in this research field since they are among the oldest stellar systems in the MW and thus bear witness to its entire past. Aims. As a natural result of galaxy formation, globular clusters did not necessarily all form in the Galaxy itself. Indeed, a fraction of them could have been formed in satellite galaxies accreted by the Milky Way over time. In recent years, there have been several attempts to constrain the nature of clusters (accreted or formed in the Milky Way itself) through the analysis of kinematic spaces, such as the E - Lz, Lperp - Lz, eccentricity - Lz, and the action space, as well as attempts to reconstruct the properties of the accretion events experienced by the Milky Way through time from this kind of analysis. This work aims to test a widely used assumption about the clustering of the accreted populations of globular clusters in the integrals of motions space. Methods. In this paper we analyse a set of dissipationless N-body simulations that reproduce the accretion of one or two satellites with their globular cluster population on a Milky Way-type galaxy. Results. Our results demonstrate that a significant overlap between accreted and 'kinematically heated' in situ globular clusters is expected in kinematic spaces for mergers with mass ratios of 1:10. In contrast with the standard assumptions made in the literature so far, we find that accreted globular clusters do not show dynamical coherence, that is, they do not cluster in kinematic spaces. In addition, we show that globular clusters can also be found in regions dominated by stars that have a different origin (i.e. a different progenitor). This casts doubt on the association between clusters and field stars that is generally made in the literature and is used to assign them to a common origin. By means of Gaussian mixture models, we demonstrate that the overlap of clusters is not only a projection effect on specific planes but is also found when the whole set of kinematic properties (i.e. E, Lz, Lperp, eccentricity, radial, and vertical actions) is taken into account. Overall, our findings severely question the recovered accretion history of the Milky Way based on the phase-space clustering of the globular cluster population.
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