PI: Jaehyon Rhee, University of Virginia, email@example.com
Address: P.O.Box 3818, Charlottesville, VA 22903-0818, U.S.A.
CoI: Timothy C. Beers, Michigan State University
Title: A ``New" Search for Cooler, Extremely Metal-Deficient Stars in the HK-II Survey
Abstract: We propose to initiate a \it new search for cooler, extremely metal-deficient stars in the thick disk and halo of the Galaxy with [Fe/H] \le -2.0. The \it new set of metal-poor star candidates (mostly giant stars) in the HK-II survey was made from new selection methodology which makes use of Artificial Neural Network (ANN) techniques for the automated \it digital scans of the original HK survey (HK-I) and 2MASS JHK photometry. Of primary importance, the objective selection criteria obtained by use of ANNs avoids the introduction of temperature-related bias in the identification of metal- poor stars, a difficulty which limited the identification of cooler metal-poor stars with HK-I. Preliminary tests with known metal-poor stars have revealed that our new approach should result in a detection efficiency of roughly 70% for stars predicted to have [Fe/H] \le - 2.0, three times higher than obtained by HK-I. To date, we have obtained a \it new database of some 2000 cooler metal-poor candidates. Metallicities and radial velocities determined from this medium- resolution spectroscopic follow-up, combined with proper motions from ongoing and future astrometric surveys will provide full space motions for numerous extremely old stars. The results will help unravel the chemical and dynamical history of the Milky Way. The newly identified metal-poor giant stars will aid us in obtaining a more reliable metallicity distribution function of the halo, especially the metal-weak tail, and can provide an independent check on the distances and metallicities obtained for candidate Astrometric Grid giant stars in the NASA's SIM. The spectra obtained in this program will directly prove whether the ANN selection algorithm can \it really discover \it new metal-deficient stars.
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