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.
Highlights
Astronomical surveys, either spaceborne or ground-based, are gathering an unprecedented amount of data
The regularized sliced inverse regression (RSIR) inversion technique, complemented with principal component analysis (PCA) pre-processing proves to be efficient in estimating stellar parameters of A, F, G, and K-type stars
For that reason and before applying the Sliced inverse regression (SIR) process for each spectrum to be analyzed, we have reduced the dimension of these matrices by reducing the size of the original LDB using PCA as described in sec. 3.1
Summary
Astronomical surveys, either spaceborne or ground-based, are gathering an unprecedented amount of data. FIREFLY uses a χ-squared minimization fitting procedure that fits stellar population models to spectroscopic data, following an iterative best-fitting process controlled by a Bayesian information criterion Their approach is efficient to overcome the so-called “ambiguities” in the spectra. We apply techniques such as, reduction of dimensionality with PCA, and a PCA-based nearest neigbor search (Paletou et al 2015 a,b; Gebran et al 2016) complemented with a Regularized Sliced Inverse Regression (Bernard-Michel et al 2009, 2007) (RSIR) procedure in order to derive simultaneously Te , log g, [M/H] and v sin i from spectra of A, F, G, and K-type stars.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have