SIMBAD references

2013A&A...556A.121G - Astronomy and Astrophysics, volume 556A, 121-121 (2013/8-1)

Identification of metal-poor stars using the artificial neural network.


Abstract (from CDS):

Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend its usage to determine absolute magnitudes. We have constructed a library of 167 medium-resolution stellar spectra (R∼1200) covering the stellar temperature range of 4200 to 8000K, log g range of 0.5 to 5.0, and [Fe/H] range of -3.0 to +0.3dex. This empirical spectral library was used to train ANNs, yielding an accuracy of 0.3dex in [Fe/H], 200K in temperature, and 0.3dex in logg. We found that the independent calibrations of near-solar metallicity stars and metal-poor stars decreases the errors in Teff and log g by nearly a factor of two. We calculated Teff, log g, and [Fe/H] on a consistent scale for a large number of field stars and candidate metal-poor stars. We extended the application of this method to the calibration of absolute magnitudes using nearby stars with well-estimated parallaxes. A better calibration accuracy for MV could be obtained by training separate ANNs for cool, warm, and metal-poor stars. The current accuracy of MV calibration is ±0.3mag. A list of newly identified metal-poor stars is presented. The MV calibration procedure developed here is reddening-independent and hence may serve as a powerful tool in studying galactic structure.

Abstract Copyright:

Journal keyword(s): stars: solar-type - stars: fundamental parameters

VizieR on-line data: <Available at CDS (J/A+A/556/A121): table1.dat table2.dat refs.dat>

Status at CDS : All or part of tables of objects could be ingested in SIMBAD with priority 2.

Simbad objects: 200

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