Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

Abstract

Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We estimate class labels according to two classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, to use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. We explore the performance of principal component analysis (PCA), linear discriminant analysis (LDA), random forest classification and self-organizing maps (SOM). We pre-process the photometric time series by combining a double Gaussian profile fit and a smoothing spline, in order to de-noise and interpolate the observed light curves. We achieve further denoising, and selected the most important variability elements from the light curves using PCA. We perform supervised classification using random forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimate the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets.

arXiv comment20 pages, 22 figures. Accepted for publication in A&A

References in corpus (25)

• The Sloan Digital Sky Survey: Technical Summary
• The Gaia mission
• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems
• Accurate masses and radii of normal stars: modern results and applications
• Kepler Eclipsing Binary Stars. VII. The Catalog of Eclipsing Binaries Found in the Entire Kepler Data-Set
• Kepler Eclipsing Binary Stars. II. 2165 Eclipsing Binaries in the Second Data Release
• A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding
• Random forest automated supervised classification of Hipparcos periodic variable stars
• Automated Transient Identification in the Dark Energy Survey
• SOMz: photometric redshift PDFs with self organizing maps and random atlas
• The extended Baryon Oscillation Spectroscopic Survey (eBOSS): a cosmological forecast
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• Light curve analysis of Variable stars using Fourier decomposition and Principal component analysis
• Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs
• Automated classification of Hipparcos unsolved variables
• Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification
• Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
• Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
• Dynamics of the Lyman alpha and C IV emitting gas in 3C 273
• Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts
• Principal Component Analysis of RR Lyrae light curves
• Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia

Cited by in corpus (9)

• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Binary Stars in the New Millennium
• A review of unsupervised learning in astronomy
• Unsupervised classification of eclipsing binary light curves through k-medoids clustering
• Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
• Classification of OGLE eclipsing binary stars based on their morphology type with Locally Linear Embedding
• Clustering of eclipsing binary light curves through functional principal component analysis
• Variable binaries and variables in binaries in the Binary Star Database BDB
• Classification of Eclipsing Binary Light Curves in Gaia DR3: A Machine Learning Approach