Photometric identification of blue horizontal branch stars
  We investigate the performance of some common machine learning techniques in
identifying BHB stars from photometric data. To train the machine learning
algorithms, we use previously published spectroscopic identifications of BHB
stars from SDSS data. We investigate the performance of three different
techniques, namely k nearest neighbour classification, kernel density
estimation and a support vector machine (SVM). We discuss the performance of
the methods in terms of both completeness and contamination. We discuss the
prospect of trading off these values, achieving lower contamination at the
expense of lower completeness, by adjusting probability thresholds for the
classification. We also discuss the role of prior probabilities in the
classification performance, and we assess via simulations the reliability of
the dataset used for training. Overall it seems that no-prior gives the best
completeness, but adopting a prior lowers the contamination. We find that the
SVM generally delivers the lowest contamination for a given level of
completeness, and so is our method of choice. Finally, we classify a large
sample of SDSS DR7 photometry using the SVM trained on the spectroscopic
sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars.
We derive photometric parallaxes and demonstrate that our results are
reasonable by comparing to known distances for a selection of globular
clusters. We attach our classifications, including probabilities, as an
electronic table, so that they can be used either directly as a BHB star
catalogue, or as priors to a spectroscopic or other classification method. We
also provide our final models so that they can be directly applied to new data.
