Volume 644, December 2020
|Number of page(s)||29|
|Section||Galactic structure, stellar clusters and populations|
|Published online||16 December 2020|
The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks⋆
Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
2 Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12–14, 69120 Heidelberg, Germany
3 Lund Observatory, Department of Astronomy and Theoretical Physics, Lund University, Box 43 22100 Lund, Sweden
4 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Nice, France
5 Saint Martin’s University, 5000 Abbey Way SE, Lacey, WA 98503, USA
6 University of Ljubljana, Faculty of Mathematics and Physics, Jadranska 19, 1000 Ljubljana, Slovenia
7 Institut de Ciències del Cosmos, Universitat de Barcelona (IEEC-UB), Martí i Franquès 1, 08028 Barcelona, Spain
8 Observatoire Astronomique de Strasbourg, Université de Strasbourg, CNRS, 11 Rue de l’Université, 67000 Strasbourg, France
9 The Johns Hopkins University, Department of Physics and Astronomy, 3400 N. Charles Street, Baltimore, MD 21218, USA
10 Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
11 Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia
12 E.A. Milne Centre for Astrophysics, University of Hull, Hull HU6 7RX, UK
13 Department of Physics and Astronomy, University of Victoria, Victoria, BC V8P 5C2, Canada
14 CYM Physics Building, The University of Hong Kong, Pokfulam, Hong Kong SAR, PR China
15 The Laboratory for Space Research, Hong Kong University, Cyberport 4, Hong Kong SAR, PR China
16 Department of Physics and Astronomy, Macquarie University, Sydney, NSW 2109, Australia
17 Western Sydney University, Locked Bag 1797, Penrith South, NSW 2751, Australia
18 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking RH5 6NT, UK
Accepted: 23 September 2020
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 R = 22 500) 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 ALL_WISE 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.
Key words: Galaxy: abundances / Galaxy: stellar content / stars: abundances / techniques: spectroscopic / methods: data analysis
The catalogue of atmospheric parameters and chemical abundances presented in Sect. 10 is publicly available on the RAVE website: https://doi.org/10.17876/rave/dr.6/020.
© ESO 2020
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