The EB Factory Project. II. Validation with the Kepler Field in Preparation for K2 and TESS
arXiv:1409.3237 · doi:10.1088/0004-6256/148/6/125
Abstract
Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical "by eye" human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline - the Eclipsing Binary Factory (EBF) - that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler "Q3" Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ~2,600 EBs. When we require EB classification with at least 90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32% respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62% respectively. Through our processing of the entire Kepler "Q3" dataset, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.
Accepted by AJ
References in corpus (10)
- The K2 Mission: Characterization and Early results
- An eclipsing binary distance to the Large Magellanic Cloud accurate to 2 per cent
- The Araucaria Project. The distance to the Small Magellanic Cloud from late-type eclipsing binaries
- A Surprising Reversal of Temperatures in the Brown-Dwarf Eclipsing Binary 2MASS J05352184-0546085
- Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project
- Identification, Classifications, and Absolute Properties of 773 Eclipsing Binaries Found in the TrES Survey
- Deep MMT Transit Survey of the Open Cluster M37 I: Observations and Cluster Parameters
- A Fast Chi-squared Technique For Period Search of Irregularly Sampled Data
- A Photometric Survey for Variables and Transits in the Field of Praesepe with KELT
- The EB Factory Project I. A Fast, Neural Net Based, General Purpose Light Curve Classifier Optimized for Eclipsing Binaries