The INT Search for Metal-Poor Stars: Spectroscopic Observations and Classification via Artificial Neural Networks

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Abstract
With the dual aims of enlarging the list of extremely metal-poor stars identified in the Galaxy and boosting the numbers of moderately metal-deficient stars in directions that sample the rotational properties of the thick disk, we have used the 2.5 m Isaac Newton Telescope and the Intermediate Dispersion Spectrograph to carry out a survey of brighter (primarily northern hemisphere) metal-poor candidates selected from the HK objective–prism–interference-filter survey of Beers and collaborators. Over the course of only three observing runs (15 nights) we have obtained medium-resolution (λ/δλ 2000) spectra for 1203 objects (V 11–15). Spectral absorption-line indices and radial velocities have been measured for all the candidates. Metallicities, quantified by [Fe/H], and intrinsic (B-V)0 colors have been estimated for 731 stars with effective temperatures cooler than roughly 6500 K by using artificial neural networks (ANNs) trained with spectral indices. We show that this method performs as well as a previously explored Ca II K calibration technique, yet it presents some practical advantages. Among the candidates in our sample we identify 195 stars with [Fe/H] ≤ -1.0, 67 stars with [Fe/H] ≤ -2.0, and 12 new stars with [Fe/H] ≤ -3.0. Although the effective yield of metal-poor stars in our sample is not as large as that in previous HK survey follow-up programs, the rate of discovery per unit of telescope time is quite high. Further development of the ANN technique, with the networks being fed the entire spectrum, rather than just the spectral indices, holds the promise to produce fast, accurate, multidimensional spectral classifications (with the associated physical parameter estimates), as is required to process the large data flow provided by present and future instrumentation.
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