The EB Factory Project I. A Fast, Neural Net Based, General Purpose Light Curve Classifier Optimized for Eclipsing Binaries

TL;DR

This paper introduces a fast, neural network-based light curve classifier optimized for eclipsing binaries and other variable stars, suitable for large surveys like LSST, with high accuracy demonstrated on existing data.

Contribution

A novel, efficient neural network classifier for light curves that is optimized for speed and broad applicability, including a geometric representation of light curves and high performance metrics.

Findings

Achieves 98% retrieval rate for eclipsing binaries

High accuracy for RR Lyrae, Mira, and delta Scuti stars

Performs well on noisy data when trained with noisy exemplars

Abstract

We describe a new neural-net based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as LSST. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98\% and a false-positive rate of 2\% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low ($\sim$60\%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.