John M. Brewer

TL;DR: In this article, a catalog of uniformly determined stellar properties and abundances for 1,617 F, G, and K stars using an automated spectral synthesis modeling procedure was presented, and all stars were observed using a single image.

TL;DR: In this paper, a catalog of uniformly determined stellar properties and abundances for 1626 F, G, and K stars using an automated spectral synthesis modeling procedure was presented using the HIRES spectrograph at Keck Observatory.

TL;DR: In this article, a methodology for variable-star classification using machine learning techniques has been proposed, which can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements.

TL;DR: In this paper, the authors made use of data from the first public release of the WASP data (Butters et al. 2010) as provided by the NASA Exoplanet Archive, which is operated by the California Institute of Technology under contract with the National Aeronautics and Space Administration under the ERC grant number 279973.

TL;DR: A methodology for variable-star classification, drawing from modern machine-learning techniques, which is effective for identifying samples of specific science classes and presents the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier.