Statistics – Methodology
Scientific paper
Aug 2000
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000phdt........13r&link_type=abstract
Thesis (PhD). MICHIGAN STATE UNIVERSITY
Statistics
Methodology
Scientific paper
In this thesis I have developed 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 Galaxy.
Automated computer scans of the 308 photographic plates in the HK objective-prism / interference-filter survey of Beers and colleagues have been carried out with the Automatic Plate Measuring (APM) machine in Cambridge, England. Highly automated software tools have been developed in order to identify useful spectra and remove unusable spectra, to locate the positions of the Ca II H (3969 Å) and K (3933 Å) absorption 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 degrees of the South Galactic Pole) were extracted. Of these, 221,670 (75.4%) proved to be useful for subsequent analysis.
I have 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 6 groups separated by temperature and metal abundance, based on two accurately measured parameters -- the de-reddened 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 Å band, the K18 index, as measured from follow-up medium-resolution spectroscopy taken during the course of the 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 one-sigma scatter of SBI = 0.13 dex, and an overall correction 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 one-sigma scatter in the range 1.2 < SBI < 1.4 Å, depending on the color and strength of the line. By feeding on calibrated, multiple-band, photographic measurements of apparent magnitudes, 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 0.13 < SBI < 0.16 magnitudes.
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 SBI = 0.78 dex, and to carry out classifications with an overall correction rate of 40%. By comparison with a large sample of known metal-poor stars, on the order of 60% of the candidates predicted to have a metallicity [Fe/H] < -2.0 indeed fell in this region of abundance (representing a three-fold improvement over the visual selection criteria previously employed in the HK survey). The recovery rate indicated that at least 30% of all such stars in our sample would be identified in a blind sampling, limited, for the most part, by the lack of accurate color information. Finally we report 481 extremely metal-poor star candidates in 10 plates of the HK survey, selected by our newly developed methodology.
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