The O stars in the VLT-FLAMES Tarantula Survey

The VLT-FLAMES Tarantula Survey (VFTS) has secured mid-resolution spectra of over 300 O-type stars in the 30 Doradus region of the Large Magellanic Cloud. A homogeneous analysis of such a large sample requires automated techniques, an approach that will also be needed for the upcoming analysis of the Gaia surveys of the Northern and Southern Hemisphere supplementing the Gaia measurements. We point out the importance of Gaia for the study of O stars, summarize the O star science case of VFTS and present a test of the automated modeling technique using synthetically generated data. This method employs a genetic algorithm based optimization technique in combination with fastwind model atmospheres. The method is found to be robust and able to recover the main photospheric parameters accurately. Precise wind parameters can be obtained as well, however, as expected, for dwarf stars the rate of acceleration of the ow is poorly constrained.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.