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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.
We have analyzed new HST/ACS and HST/WFC3 imaging in F475W and F814W of two previously-unobserved fields along the M31 minor axis to confirm our previous constraints on the shape of M31s inner stellar halo. Both of these new datasets reach a depth of
Context: Blue horizontal-branch stars are very old objects that can be used as markers in studies of the Galactic structure and formation history. To create a clean sky catalogue of blue horizontal-branch stars, we cross-matched the Gaia data release
The distribution of Milky Way halo blue horizontal-branch (BHB) stars is examined using action-based extended distribution functions (EDFs) that describe the locations of stars in phase space, metallicity, and age. The parameters of the EDFs are fitt
Blue horizontal-branch stars are Population II objects which are burning helium in their core and possess a hydrogen-burning shell and radiative envelope. Because of their low rotational velocities, diffusion has been predicted to work in their atmos
We use 666 blue horizontal branch (BHB) stars from the 2Qz redshift survey to map the Galactic halo in four dimensions (position, distance and velocity). We find that the halo extends to at least 100 kpc in Galactocentric distance, and obeys a single