Bibcode
Re Fiorentin, P.; Bailer-Jones, C. A. L.; Lee, Y. S.; Beers, T. C.; Sivarani, T.; Wilhelm, R.; Allende Prieto, C.; Norris, J. E.
Bibliographical reference
Astronomy and Astrophysics, Volume 467, Issue 3, June I 2007, pp.1373-1387
Advertised on:
6
2007
Journal
Citations
85
Refereed citations
74
Description
We present techniques for the estimation of stellar atmospheric
parameters (T_eff, log~g, [Fe/H]) for stars from the SDSS/SEGUE survey.
The atmospheric parameters are derived from the observed
medium-resolution (R = 2000) stellar spectra using non-linear regression
models trained either on (1) pre-classified observed data or (2)
synthetic stellar spectra. In the first case we use our models to
automate and generalize parametrization produced by a preliminary
version of the SDSS/SEGUE Spectroscopic Parameter Pipeline (SSPP). In
the second case we directly model the mapping between synthetic spectra
(derived from Kurucz model atmospheres) and the atmospheric parameters,
independently of any intermediate estimates. After training, we apply
our models to various samples of SDSS spectra to derive atmospheric
parameters, and compare our results with those obtained previously by
the SSPP for the same samples. We obtain consistency between the two
approaches, with RMS deviations on the order of 150 K in T_eff, 0.35 dex
in log~g, and 0.22 dex in [Fe/H]. The models are applied to
pre-processed spectra, either via Principal Component Analysis (PCA) or
a Wavelength Range Selection (WRS) method, which employs a subset of the
full 3850-9000Å spectral range. This is both for computational
reasons (robustness and speed), and because it delivers higher accuracy
(better generalization of what the models have learned). Broadly
speaking, the PCA is demonstrated to deliver more accurate atmospheric
parameters when the training data are the actual SDSS spectra with
previously estimated parameters, whereas WRS appears superior for the
estimation of log~g via synthetic templates, especially for lower
signal-to-noise spectra. From a subsample of some 19 000 stars with
previous determinations of the atmospheric parameters, the accuracies of
our predictions (mean absolute errors) for each parameter are T_eff to
170/170 K, log~g to 0.36/0.45 dex, and [Fe/H] to 0.19/0.26 dex, for
methods (1) and (2), respectively. We measure the intrinsic errors of
our models by training on synthetic spectra and evaluating their
performance on an independent set of synthetic spectra. This yields RMS
accuracies of 50 K, 0.02 dex, and 0.03 dex on T_eff, log~g, and [Fe/H],
respectively. Our approach can be readily deployed in an automated
analysis pipeline, and can easily be retrained as improved stellar
models and synthetic spectra become available. We nonetheless emphasise
that this approach relies on an accurate calibration and pre-processing
of the data (to minimize mismatch between the real and synthetic data),
as well as sensible choices concerning feature selection. From an
analysis of cluster candidates with available SDSS spectroscopy (M 15, M
13, M 2, and NGC 2420), and assuming the age, metallicity, and distances
given in the literature are correct, we find evidence for small
systematic offsets in T_eff and/or log~g for the parameter estimates
from the model trained on real data with the SSPP. Thus, this model
turns out to derive more precise, but less accurate, atmospheric
parameters than the model trained on synthetic data.