dc.contributor.author |
Giridhar, S |
|
dc.contributor.author |
Goswami, A |
|
dc.contributor.author |
Kunder, A |
|
dc.contributor.author |
Muneer, S |
|
dc.contributor.author |
Selvakumar, G |
|
dc.date.accessioned |
2013-08-20T10:13:37Z |
|
dc.date.available |
2013-08-20T10:13:37Z |
|
dc.date.issued |
2013-08 |
|
dc.identifier.citation |
Astronomy & Astrophysics, Vol. 556, A121 |
en |
dc.identifier.issn |
0004-6361 |
|
dc.identifier.uri |
http://hdl.handle.net/2248/6314 |
|
dc.description.abstract |
Context. Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies.
Aims. We search for metal-poor stars using the artificial neural network (ANN) and extend its usage to determine absolute magnitudes.
Methods. We have constructed a library of 167 medium-resolution stellar spectra (R ~ 1200) covering the stellar temperature range of 4200 to 8000 K, log g range of 0.5 to 5.0, and [Fe/H] range of −3.0 to +0.3 dex. This empirical spectral library was used to train ANNs, yielding an accuracy of 0.3 dex in [Fe/H] , 200 K in temperature, and 0.3 dex in log g. 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.
Results. 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 ±0.3 mag.
Conclusions. 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. |
en |
dc.language.iso |
en |
en |
dc.publisher |
EDP Sciences |
en |
dc.relation.uri |
http://dx.doi.org/10.1051/0004-6361/201219918 |
en |
dc.relation.uri |
http://www.arxiv.org/abs/1307.6308 |
en |
dc.rights |
© ESO, 2013 |
en |
dc.subject |
Stars: solar-type |
en |
dc.subject |
Stars: fundamental parameters |
en |
dc.title |
Identification of metal-poor stars using the artificial neural network |
en |
dc.type |
Article |
en |