A Detection Metric Designed for O'Connell Effect Eclipsing Binaries

Abstract

We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification of eclipsing binaries that demonstrate a feature known as the O'Connell effect. Our proposed methodology maps stellar variable observations to a new representation known as distribution fields (DFs). Given this novel representation, we develop a metric learning technique directly on the DF space that is capable of specifically identifying our stars of interest. The metric is tuned on a set of labeled eclipsing binary data from the Kepler survey, targeting particular systems exhibiting the O'Connell effect. The result is a conservative selection of 124 potential targets of interest out of the Villanova Eclipsing Binary Catalog. Our framework demonstrates favorable performance on Kepler eclipsing binary data, taking a crucial step in preparing the way for large-scale data volumes from next-generation telescopes such as LSST and SKA.

References in corpus (28)

• A Computational Guide to Physics of Eclipsing Binaries. Paper I. Demonstrations and Perspectives
• Kepler Eclipsing Binary Stars. VII. The Catalog of Eclipsing Binaries Found in the Entire Kepler Data-Set
• Highly comparative time-series analysis: The empirical structure of time series and their methods
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Periodograms for Multiband Astronomical Time Series
• Random forest automated supervised classification of Hipparcos periodic variable stars
• Exploring the Variable Sky with LINEAR. III. Classification of Periodic Light Curves
• Searching for Exoplanets Using Artificial Intelligence
• A recurrent neural network for classification of unevenly sampled variable stars
• K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
• A comparison of period finding algorithms
• Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey
• Light curve analysis of Variable stars using Fourier decomposition and Principal component analysis
• A Machine Learning Technique to Identify Transit Shaped Signals
• Machine Learning Techniques for Stellar Light Curve Classification
• Automated classification of variable stars for ASAS 1-2 data
• Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer
• A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves
• Machine-assisted discovery of relationships in astronomy
• Automatic Classification of Variable Stars in Catalogs with missing data
• Automated classification of periodic variable stars{Improved methodology for the automated classification of periodic variable stars}
• The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared
• An improved quasar detection method in EROS-2 and MACHO LMC datasets
• Unsupervised Classification of Variable Stars
• Stellar variability on time-scales of minutes: results from the first 5 years of the Rapid Temporal Survey (RATS)
• Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset
• Variable Star Signature Classification using Slotted Symbolic Markov Modeling
• Preliminary Analysis of ULPC Light Curves Using Fourier Decomposition Technique

Cited by in corpus (5)

• Planetary nebulae seen with TESS: Discovery of new binary central star candidates from Cycle 1
• Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning
• Variable Star Classification Using Multi-View Metric Learning
• Multiband analysis of the O'Connell effect in 14 eclipsing binaries
• Characteristics of Kepler Eclipsing Binaries Displaying a Significant O'Connell Effect