Suchergebnisse

[Context] Thanks to ongoing efforts to compute accurate stellar ages, we are able to characterise stars in different regions of the Milky Way. The Gaia and Kepler space-missions, along with ground-based spectroscopic surveys such as APOGEE, provide a unique way to study the chemo-kinematics relations as a function of age through the Galactic stellar populations and provide new constraints to Galactic evolution models. [Aims] We investigate the properties of the double sequences of the Milky Way discs visible in the [α/Fe] versus [Fe/H] diagram, which are usually associated to the chemical thin and thick discs at the solar circle. In the framework of Galactic formation and evolution, we discuss the complex relationships between age, metallicity, [α/Fe], and the radial, azimuthal, and vertical components of the space velocities. [Methods] We study stars with measured chemical and seismic properties from the APOGEE spectroscopic survey and the Kepler satellite, respectively. In addition, astrometry from the Gaia satellite is available for the majority of the sample. We separate the [α/Fe]-[Fe/H] diagram into three stellar populations: the thin disc, the high-α metal-poor thick disc, and the high-α metal-rich thick disc and characterise each of these in the age-chemo-kinematics parameter space. Because of the model-dependent nature of the ages inferred from asteroseismology, and because they depend on the quality of the input spectroscopic information, we compare results obtained from different APOGEE data releases (DR14 and DR16). We also use age determinations from two recent works in the literature. In addition, we use the Besançon stellar populations synthesis model to highlight selection biases and mechanisms (such as mergers and secular evolution) not included in the model. [Results] The thin disc exhibits a flat age-metallicity relation while [α/Fe] increases with stellar age. We confirm no correlation between radial and vertical velocities with [Fe/H], [α/Fe], and age for each stellar population. Considering both samples, Vφ decreases with age for the thin disc, while Vφ increases with age for the high-α metal-poor thick disc. We show that this difference is not due to sample selection. Although the age distribution of the high-α metal-rich thick disc is very close to that of the high-α metal-poor thick disc between 7 and 14 Gyr, its kinematics seems to follow that of the thin disc. This feature, not predicted by the hypotheses included in the Besançon Galaxy Model, suggests a different origin and history for this population. Finally, we show that there is a maximum dispersion of the vertical velocity, σZ, with age for the high-α metal-poor thick disc around 8 Gyr. The comparisons with the Besançon Galaxy Model simulations suggest a more complex chemo-dynamical scheme to explain this feature, most likely including mergers and radial migration effects. ; F.F., A.F., R.M., M.R., T.A. acknowledge support by the Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE) through grant RTI2018-095076-B-C21, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia "María de Maeztu") through grant CEX2019-000918-M, the Ramon y Cajal Fellowship RYC2018-025968-I. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 800502. AM acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772293 – project ASTEROCHRONOMETRY, https://www.asterochronometry.eu.

[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. ; Funding for RAVE has been provided by: the Leibniz-Institut für Astrophysik Potsdam (AIP); the Australian Astronomical Observatory; the Australian National University; the Australian Research Council; the French National Research Agency (Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU with INP and IN2P3, co-funded by CEA and CNES); the German Research Foundation (SPP 1177 and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns Hopkins University; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the Netherlands Research School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency (research core funding no. P1-0188); the Swiss National Science Foundation; the Science & Technology Facilities Council of the UK; Opticon; Strasbourg Observatory; and the Universities of Basel, Groningen, Heidelberg, and Sydney. TZ acknowledges financial support of the Slovenian Research Agency (research core funding No. P1-0188) and of the ESA project PHOTO2CHEM (C4000127986). FA is grateful for funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 800502. This work has made use of data from the European Space Agency (ESA) mission Gaia (http://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.