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 Components Analysis ( PCA ) or a Wavelength Range Selection ( WRS ) method , which employs a subset of the full 3850–9000 \AA 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 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 .