Results of CNN classifications (ravedr6.dr6_cnn)

Description

The table provides atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey as result of a Convolutional Neural Network application. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data, without applying any priors in the form of stellar evolutionary models.

Attribution

Funding for Rave has been provided by:

• the Leibniz Institute for Astrophysics Potsdam (AIP);
• the Australian Astronomical Observatory;
• the Australian National University;
• the Australian Research Council;
• the French National Research Agency;
• 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;
• the Swiss National Science Foundation;
• the Science & Technology FacilitiesCouncil of the UK;
• Opticon;
• Strasbourg Observatory;
• the Universities of Basel, Groningen, Heidelberg and Sydney.

This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the GaiaData Processing and Analysis Consortium (DPAC, https://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.

Columns