| 1. |
- Guiglion, G., et al.
(author)
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The RAdial Velocity Experiment (RAVE) : Parameterisation of RAVE spectra based on convolutional neural networks
- 2020
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 644
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Journal article (peer-reviewed)abstract
- Context Data-driven methods play an increasingly important role in the field of astrophysics In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). Aims. We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. Methods. We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution Ra22 500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R ∼ 7500) overlapping with the APOGEE DR16 data as well as broad-band ALLWISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. Results. We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-Trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey. Conclusions. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.
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| 2. |
- Barbuy, B., et al.
(author)
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Light elements Na and Al in 58 bulge spheroid stars from APOGEE
- 2023
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In: Monthly Notices of the Royal Astronomical Society. - 0035-8711. ; 526:2, s. 2365-2376
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Journal article (peer-reviewed)abstract
- We identified a sample of 58 candidate stars with metallicity [Fe/H]-0.8 that likely belong to the old bulge spheroid stellar population, and analyse their Na and Al abundances from Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra. In a previous work, we inspected APOGEE-Stellar Parameter and Chemical Abundance Pipeline abundances of C, N, O, Mg, Al, Ca, Si, and Ce in this sample. Regarding Na lines, one of them appears very strong in about 20 per cent of the sample stars, but it is not confirmed by other Na lines, and can be explained by sky lines, which affect the reduced spectra of stars in a certain radial velocity range. The Na abundances for 15 more reliable cases were taken into account. Al lines in the H band instead appear to be very reliable. Na and Al exhibit a spread in abundances, whereas no spread in N abundances is found, and we found no correlation between them, indicating that these stars could not be identified as second-generation stars that originated in globular clusters. We carry out the study of the behaviour of Na and Al in our sample of bulge stars and literature data by comparing them with chemodynamical evolution model suitable for the Galactic bulge. The Na abundances show a large spread, and the chemodynamical models follow the main data, whereas for aluminum instead, the models reproduce very satisfactorily the nearly secondary-element behaviour of aluminum in the metallicity range below [Fe/H]-1.0. For the lower-metallicity end ([Fe/H <-2.5), hypernovae are assumed to be the main contributor to yields.
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| 3. |
- Souza, S. O., et al.
(author)
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Chrono-chemodynamical analysis of the globular cluster NGC 6355 : Looking for the fundamental bricks of the Bulge
- 2023
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 671
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Journal article (peer-reviewed)abstract
- 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.
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| 4. |
- Carrillo, I., et al.
(author)
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Kinematics with GAIA DR2 : The force of a dwarf
- 2019
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In: Monthly Notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 1365-2966 .- 0035-8711. ; 490:1, s. 797-812
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Journal article (peer-reviewed)abstract
- We use Gaia DR2 astrometric and line-of-sight velocity information combined with two sets of distances obtained with a Bayesian inference method to study the 3D velocity distribution in the Milky Way disc. We search for variations in all Galactocentric cylindrical velocity components (Vφ, VR, and Vz) with Galactic radius, azimuth, and distance from the disc mid-plane. We confirm recent work showing that bulk vertical motions in the R–z plane are consistent with a combination of breathing and bending modes. In the x–y plane, we show that, although the amplitudes change, the structure produced by these modes is mostly invariant as a function of distance from the plane. Comparing to two different Galactic disc models, we demonstrate that the observed patterns can drastically change in short time intervals, showing the complexity of understanding the origin of vertical perturbations. A strong radial VR gradient was identified in the inner disc, transitioning smoothly from 16 km s−1 kpc−1 at an azimuth of 30◦ < φ < 45◦ ahead of the Sun-Galactic centre line to −16 km s−1 kpc−1 at an azimuth of −45◦ < φ < −30◦ lagging the solar azimuth. We use a simulation with no significant recent mergers to show that exactly the opposite trend is expected from a barred potential, but overestimated distances can flip this trend to match the data. Alternatively, using an N-body simulation of the Sagittarius dwarf–Milky Way interaction, we demonstrate that a major recent perturbation is necessary to reproduce the observations. Such an impact may have strongly perturbed the existing bar or even triggered its formation in the last 1–2 Gyr.
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| 5. |
- 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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