Pattern recognition in spectra

Frontiers in Theoretical and Applied Physics/UAE 2017

Gebran, M.^1 ; Paletou, F.^2,3

Department of Physics and Astronomy, Notre Dame University-Louaize, PO Box 72, Zouk Mikaël, Lebanon^1

Université de Toulouse, UPS-Observatoire Midi-Pyrénées, IRAP, Toulouse

31000, France^2

CNRS, Institut de Recherche en Astrophysique et Planétologie, 14 av. E. Belin, Toulouse

31400, France^3

关键词: Automated procedures;    Effective temperature;    Learning database;    Model atmosphere;    Planetary surfaces;    Rotational velocity;    Stellar spectra;    Synthetic spectra;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/869/1/012075/pdf
DOI  :  10.1088/1742-6596/869/1/012075

来源: IOP

【 摘 要 】

We present a new automated procedure that simultaneously derives the effective temperature Teff, surface gravity log g, metallicity [Fe/H], and equatorial projected rotational velocity vesin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones.