Machine-assisted discovery of relationships in astronomy

Creators: Graham, Matthew J.; Djorgovski, S. G.; Mahabal, Ashish A.; Donalek, Ciro; Drake, Andrew J.

Abstract

High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focusing on an evolutionary programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships – the Hertzsprung–Russell diagram and the Fundamental Plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as the Large Synoptic Survey Telescope (LSST). We find that comparable results to more familiar techniques, such as decision trees, are achievable. Finally, we consider the reality of the relationships discovered and how this can be used for feature selection and extraction.

Additional Information

© 2013 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2013 February 20. Received 2013 February 19; in original form 2012 October 4. First published online: March 20, 2013. We thank Hod Lipson and Michael Schmidt for useful discussions and their kind assistance with the EUREQA software. We also thank the anonymous referee for their useful comments which helped improve this paper. This work was supported in part by the NSF grants AST-0909182 and IIS-1118041, by the W. M. Keck Institute for Space Studies, and by the US Virtual Astronomical Observatory, itself supported by the NSF grant AST-0834235. This research has made use of data obtained from or software provided by the US Virtual Astronomical Observatory, which is sponsored by the National Science Foundation and the National Aeronautics and Space Administration. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France, and the International Variable Star Index (VSX) data base, operated at AAVSO, Cambridge, Massachusetts, USA. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation and the U.S. Department of Energy Office of Science. The SDSS-III website is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University,University of Virginia, University of Washington and Yale University.

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