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Seabroke, G. M., et al. ; [Context] Gaia's Early Third Data Release (EDR3) does not contain new radial velocities because these will be published in Gaia's full third data release (DR3), expected in the first half of 2022. To maximise the usefulness of EDR3, Gaia's second data release (DR2) sources (with radial velocities) are matched to EDR3 sources to allow their DR2 radial velocities to also be included in EDR3. This presents two considerations: (i) a list of 70 365 sources with potentially contaminated DR2 radial velocities has been published; and (ii) EDR3 is based on a new astrometric solution and a new source list, which means sources in DR2 may not be in EDR3. [Aims] The two aims of this work are: (i) investigate the list in order to improve the DR2 radial velocities being included in EDR3 and to avoid false-positive hypervelocity candidates; and (ii) match the DR2 sources (with radial velocities) to EDR3 sources. [Methods] Thetwo methods of this work are: (i) unpublished, preliminary DR3 radial velocities of sources on the list, and high-velocity stars not on the list, are compared with their DR2 radial velocities to identify and remove contaminated DR2 radial velocities from EDR3; and (ii) proper motions and epoch position propagation is used to attempt to match all sources with radial velocities in DR2 to EDR3 sources. The comparison of DR2 and DR3 radial velocities is used to resolve match ambiguities. [Results] EDR3 contains 7 209 831 sources with a DR2 radial velocity, which is 99.8% of sources with a radial velocity in DR2 (7 224 631). 14 800 radial velocities from DR2 are not propagated to any EDR3 sources because (i) 3871 from the list are found to either not have a DR3 radial velocity or it differs significantly from its DR2 value, and five high-velocity stars not on the list are confirmed to have contaminated radial velocities, in one case because of contamination from the non-overlapping Radial Velocity Spectrometer windows of a nearby, bright star; and (ii) 10 924 DR2 sources could not be satisfactorily matched to any EDR3 sources, so their DR2 radial velocities are also missing from EDR3. [Conclusions] The reliability of radial velocities in EDR3 has improved compared to DR2 because the update removes a small fraction of erroneous radial velocities (0.05% of DR2 radial velocities and 5.5% of the list). Lessons learnt from EDR3 (e.g. bright star contamination) will improve the radial velocities in future Gaia data releases. The main reason for radial velocities from DR2 not propagating to EDR3 is not related to DR2 radial velocity quality. It is because the DR2 astrometry is based on one component of close binary pairs, while EDR3 astrometry is based on the other component, which prevents these sources from being unambiguously matched. ; This work was supported by the Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE) through grants RTI2018-095076-B-C21, ESP2016-80079-C2-1-R, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu') through grants MDM-2014-0369 and CEX2019-000918-M. T.A. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement no. 745617 and from the Ramon y Cajal Fellowship RYC2018-025968-I. The Digitized Sky Surveys were produced at the Space Telescope Science Institute under US Government grant NAG W-2166.

[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.