Results of CNN classifications (ravedr6.dr6_cnn)

The table has 426574 rows, 30 columns.

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

Name Type UCD Unit Description
rave_obs_id char meta.id

Unique Identifier for RAVE objects, combines Observation Date, Fieldname, Fibernumber

source_id long meta.id

Unique gaia source identifier (unique within a particular Data Release), here DR2

snr_cnn float stat.snr

Signal/Noise Ratio from RAVE DR6 ??

teff_cnn float phys.temperature.effective K

Effective temperature from CNN

teff_error_cnn float stat.error
phys.temperature.effective
K

Error of effective temperature from CNN

teff_flag_cnn float meta.code

Boundary flag of effective temperature

logg_cnn float phys.gravity

Log surface gravity from CNN

logg_error_cnn float phys.gravity

Error of Log surface gravity from CNN

logg_flag_cnn float meta.code

Boundary flag of Log surface gravity

m_h_cnn float phys.abund.Z

Overall metallicity from CNN

m_h_error_cnn float phys.abund.Z

Error of overall metallicity from CNN

m_h_flag_cnn float meta.code

Boundary flag of overall metallicity

fe_h_cnn float phys.abund.Z

[Fe/H] ratio from CNN

fe_h_error_cnn float phys.abund.Z

Error of [Fe/H] ratio from CNN

fe_h_flag_cnn float meta.code

Boundary flag of [Fe/H] ratio

alpha_m_cnn float phys.abund

[alpha/M] ratio from CNN

alpha_m_error_cnn float phys.abund

Error of [alpha/M] ratio from CNN

alpha_m_flag_cnn float meta.code

Boundary flag of [alpha/M] ratio

si_fe_cnn float phys.abund.Z

[Si/Fe] ratio from CNN

si_fe_error_cnn float phys.abund.Z

Error of [Si/Fe] ratio from CNN

si_fe_flag_cnn float meta.code

Boundary flag of [Si/Fe] ratio

al_fe_cnn float phys.abund.Z

[Al/Fe] ratio from CNN

al_fe_error_cnn float phys.abund.Z

Error of [Al/Fe] ratio from CNN

al_fe_flag_cnn float meta.code

Boundary flag of [Al/Fe] ratio

mg_fe_cnn float phys.abund.Z

[Mg/Fe] ratio from CNN

mg_fe_error_cnn float phys.abund.Z

Error of [Mg/Fe] ratio from CNN

mg_fe_flag_cnn float meta.code

Boundary flag of [Mg/Fe] ratio

ni_fe_cnn float phys.abund.Z

[Ni/Fe] ratio from CNN

ni_fe_error_cnn float phys.abund.Z

Error of [Ni/Fe] ratio from CNN

ni_fe_flag_cnn float meta.code

Boundary flag of [Ni/Fe] ratio