Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project

• Published in: arXiv.org
• Main Authors: Prsa, A, Guinan, E F, Devinney, E J, DeGeorge, M, Bradstreet, D H, Giammarco, J M, Alcock, C R, Engle, S G
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 10.07.2008
• Subjects: Artificial intelligence; Artificial neural networks; Binary stars; Celestial bodies; Central processing units; CPUs; Data acquisition; Data analysis; Eclipsing binary stars; Learning theory; Light curve; Mathematical models; Neural networks; Parameters; Physical properties; Physics - Astrophysics of Galaxies; Physics - Cosmology and Nongalactic Astrophysics; Physics - Earth and Planetary Astrophysics; Physics - High Energy Astrophysical Phenomena; Physics - Instrumentation and Methods for Astrophysics; Physics - Solar and Stellar Astrophysics
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.0807.1724

Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great promise in contributing to this goal. Here we apply neural network learning technology to the specific domain of eclipsing binary (EB) stars, of which only some hundreds have been rigorously analyzed, but whose numbers will reach millions in a decade. Well-analyzed EBs are a prime source of astrophysical information whose growth rate is at present limited by the need for human interaction with each EB data-set, principally in determining a starting solution for subsequent rigorous analysis. We describe the artificial neural network (ANN) approach which is able to surmount this human bottleneck and permit EB-based astrophysical information to keep pace with future data rates. The ANN, following training on a sample of 33,235 model light curves, outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega), e cos(omega), and sin i) for each input light curve data-set. The whole sample is processed in just a few seconds on a single 2GHz CPU. The obtained parameters can then be readily passed to sophisticated modeling engines. We also describe a novel method polyfit for pre-processing observational light curves before inputting their data to the ANN and present the results and analysis of testing the approach on synthetic data and on real data including fifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB) database and 2580 light curves from OGLE survey data. [abridged]