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

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 2.5m 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 (resolving power ~ 2000) spectra for 1203 objects (V ~ 11-15). Spectral absorption-line indices and radial velocities have been measured for all of the candidates. Metallicities, quantified by [Fe/H], and intrinsic (B-V)o colors have been estimated for 731 stars with effective temperatures cooler than roughly 6500 K, making use of 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 EFECTIVE YIELD of metal-poor stars in our sample is not as large as previous HK survey follow-up programs, the rate of discovery per unit of telescope time is quite high.