Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding

TL;DR

This paper introduces an automated method using Locally Linear Embedding to classify Kepler eclipsing binary light curves into morphology types, enabling efficient and accurate large-scale analysis of stellar systems.

Contribution

The paper presents a novel application of Locally Linear Embedding for automated classification of eclipsing binary light curves, improving speed and accuracy over manual methods.

Findings

High correlation with manual classifications

Efficient classification of large datasets

Identifies misclassified objects effectively

Abstract

We present an automated classification of 2165 \textit{Kepler} eclipsing binary (EB) light curves that accompanied the second \textit{Kepler} data release. The light curves are classified using Locally Linear Embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used Principal Component Analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of "detachedness" of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the \textit{Kepler} EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.