Photometric identification of BHB stars

To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from Sloan Digital Sky Survey (SDSS) data. We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation for discriminant analysis and a support vector machine (SVM).

We discuss the performance of the methods in terms of both completeness (what fraction of input BHB stars are successfully returned as BHB stars) and contamination (what fraction of contaminating sources end up in the output BHB sample). 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.