Automated Selection of Metal‐poor Stars in the Galaxy—The HK‐II Survey

In this thesis I have developed new algorithms for the efficient reduction and analysis of a large set of objective-prism data and for the reliable selection of extremely metal-poor candidate stars in the thick disk and halo of the Milky Way (the HK-II Survey). Automated computer scans of the 308 photographic wide-field ( ) plates in the original HK objective-prism/interference5 # 5 filter survey of T. C. Beers and colleagues have been carried out in collaboration with Michael J. Irwin using the Automatic Plate Measuring (APM) machine in Cambridge, England. As a result of these efforts, more than 1.5 million digital stellar spectra were obtained over a short wavelength range of 150 A, including the Ca ii H (3969 A) and K (3933 A) absorption lines. Highly automated software tools were developed in order to identify useful spectra and remove unusable spectra, to locate the positions of the Ca ii H and K lines, and to construct approximate continua. Equivalent widths of the Ca ii H and K lines were then measured directly from these reduced spectra. A subset of 294,039 spectra from 87 of the HK survey plates (located within approximately 30 of the south Galactic pole) were extracted for first-pass reduction. Of these, 221,670 (75.4%) proved to be useful for subsequent analysis. Reductions of the remainder of the plates will be completed in the near future. I have also explored new methodology, making use of an artificial neural network (ANN) analysis approach, in order to select extremely metal-poor star candidates with high efficiency. The ANNs were trained to predict metallicity, [Fe/H], and to classify stars into six groups separated by temperature and metal abundance, based on two accurately measured parameters: the dereddened broadband (B V)0 color for known HK survey stars with available photometric information and the equivalent width of the Ca ii K line in an 18 A band, the K18 index, as measured from follow-up medium-resolution spectroscopy taken during the course of the original HK survey. When provided with accurate input data, the trained networks were able to estimate [Fe/H] and to determine the class with high accuracy (with a robust estimated 1 j scatter of dex and an overall correction S p 0.13 BI rate of 91%). The ANN approach was then used in order to recover information on the K18 index and (B V)0 color directly from the APM-extracted spectra. Trained networks fed with known colors, measured peak fluxes, and the raw fluxes of the low-resolution digital spectra were able to predict the K18 index with a 1 j scatter in the range A, depending on the 1.2 A ≤ S ≤ 1.9 BI strength of the line. By feeding on calibrated, multiple-band (B, R, and I), photographic measurements of apparent magnitudes in the SuperCOSMOS Sky Surveys, peak fluxes, and the fluxes of estimated continua of the extracted APM spectra, the trained networks were able to estimate (B V)0 colors with a scatter in the range mag, depending on the avail0.11 mag ≤ S ≤ 0.21 BI able color combinations and the temperatures of the stars. From an application of the ANN approach, using the less accurate information obtained from the calibrated estimates of K18 and (B V)0 colors, it still proved possible to obtain metal abundance estimates with a scatter of dex and to S p 0.78 BI carry out classifications with an overall correction rate of 40%. By comparison with a large sample of known metal-deficient stars, on the order of 60% of the candidates predicted to have a metallicity [Fe/H indeed fell in this region of abun] ≤ 2.0 dance (representing a threefold improvement over the visual selection criteria previously employed in the original HK survey). Recent endeavors to refine methodology have shown that (B V)0 can be estimated from the 2MASS JHK photometry with better accuracy ( mag) and accordingly improved S p 0.08 BI [Fe/H] estimates ( dex) and detection efficiency S p 0.70 BI (∼70%) can also be obtained. Late spectroscopic observations support this higher detection rate of bona fide metal-poor stars among HK-II candidates predicted to have [Fe/H . ] ≤ 2.0 The ANN approach to the digital spectra has a number of advantages over the previously employed visual selection. Of primary importance, the objective selection criteria obtained by the use of ANNs avoids the introduction of temperature-related bias in the identification of metal-poor stars, a difficulty that limited the identification of cooler metal-deficient stars in the original HK survey. As a result of the HK-II Survey, we expect to obtain a new database of some 5000 cooler metal-deficient candidates, consisting mainly of giants, over ∼7000 deg (onesixth of the entire sky). Follow-up spectroscopy of these luminous and thus distant stars, and proper motions provided by ongoing and future astrometric surveys (such as the Yale/San Juan Southern Proper Motion, USNO survey, and NASA’s FAME and SIM), will help unravel the chemical and dynamical history of the Galaxy.