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; and 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 dex Log surface gravity from CNN
logg_error_cnn float phys.gravity dex 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 dex Overall metallicity from CNN
m_h_error_cnn float phys.abund.Z dex 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 dex [Fe/H] ratio from CNN
fe_h_error_cnn float phys.abund.Z dex 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 dex [alpha/M] ratio from CNN
alpha_m_error_cnn float phys.abund dex 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 dex [Si/Fe] ratio from CNN
si_fe_error_cnn float phys.abund.Z dex 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 dex [Al/Fe] ratio from CNN
al_fe_error_cnn float phys.abund.Z dex 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 dex [Mg/Fe] ratio from CNN
mg_fe_error_cnn float phys.abund.Z dex 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 dex [Ni/Fe] ratio from CNN
ni_fe_error_cnn float phys.abund.Z dex Error of [Ni/Fe] ratio from CNN
ni_fe_flag_cnn float meta.code Boundary flag of [Ni/Fe] ratio