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- Gratton, R., et al.
(författare)
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Searching for the near-infrared counterpart of Proxima c using multi-epoch high-contrast SPHERE data at VLT
- 2020
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 638
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Tidskriftsartikel (refereegranskat)abstract
- Context. Proxima Centauri is the closest star to the Sun and it is known to host an Earth-like planet in its habitable zone; very recently a second candidate planet was proposed based on radial velocities. At quadrature, the expected projected separation of this new candidate is larger than 1 arcsec, making it a potentially interesting target for direct imaging.Aims. While identification of the optical counterpart of this planet is expected to be very difficult, successful identification would allow for a detailed characterization of the closest planetary system.Methods. We searched for a counterpart in SPHERE images acquired over four years through the SHINE survey. In order to account for the expected large orbital motion of the planet, we used a method that assumes the circular orbit obtained from radial velocities and exploits the sequence of observations acquired close to quadrature in the orbit. We checked this with a more general approach that considers Keplerian motion, called K-stacker.Results. We did not obtain a clear detection. The best candidate has signal-to-noise ratio (S/N) = 6.1 in the combined image. A statistical test suggests that the probability that this detection is due to random fluctuation of noise is <1%, but this result depends on the assumption that the distribution of noise is uniform over the image, a fact that is likely not true. The position of this candidate and the orientation of its orbital plane fit well with observations in the ALMA 12 m array image. However, the astrometric signal expected from the orbit of the candidate we detected is 3 away from the astrometric motion of Proxima as measured from early Gaia data. This, together with the unexpectedly high flux associated with our direct imaging detection, means we cannot confirm that our candidate is indeed Proxima c.Conclusions. On the other hand, if confirmed, this would be the first observation in imaging of a planet discovered from radial velocities and the second planet (after Fomalhaut b) of reflecting circumplanetary material. Further confirmation observations should be done as soon as possible.
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| 2. |
- Guiglion, G., et al.
(författare)
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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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Ingår i: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 682
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Tidskriftsartikel (refereegranskat)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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