Sliced Inverse Regression: application to fundamental stellar parameters

Author:

Kassounian Sarkis1,Gebran Marwan1,Paletou Frédéric2,Watson Victor2

Affiliation:

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

2. Université Paul Sabatier , Observatoire Midi–Pyrénées , Cnrs, Cnes, IRAP, F–31400 Toulouse , France ; Cnrs, Cnes, Institut de Recherche en Astrophysique et Planétologie , 14 av. E. Belin, F–31400 Toulouse , France

Abstract

Abstract We present a method for deriving the stellar fundamental parameters. It is based on a regularized sliced inverse regression (RSIR).We first tested it on noisy synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously their atmospheric fundamental parameters: T eff., log g, [M/H] and v sin i. Different learning databases were calculated using a range of sampling in T eff., log g, v sin i, and [M/H]. Combined with a principal component analysis (PCA) nearest neighbors (NN) search, the size of the learning database is reduced. A Tikhonov regularization is applied, given the ill-conditioning of SIR. For all spectral types, decreasing the size of the learning database allowed us to reach internal accuracies better than PCA-based NN-search using larger learning databases. For each analyzed parameter, we have reached internal errors that are smaller than the sampling step of the parameter. We have also applied the technique to a sample of observed FGK and A stars. For a selection of well-studied stars, the inverted parameters are in agreement with the ones derived in previous studies. The RSIR inversion technique, complemented with PCA pre-processing proves to be efficient in estimating stellar parameters of A, F, G, and K-type stars.

Publisher

Walter de Gruyter GmbH

Subject

Space and Planetary Science,Astronomy and Astrophysics

Reference60 articles.

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