Journal
ASTROPHYSICAL JOURNAL
Volume 691, Issue 1, Pages 32-42Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0004-637X/691/1/32
Keywords
galaxies: distances and redshifts; galaxies: fundamental parameters; galaxies: statistics; methods: data analysis; methods: statistical
Categories
Funding
- NSF [0707059]
- ONR [N00014-08-1-0673]
- Alfred P. Sloan Foundation
- Participating Institutions
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
- American Museum of Natural History
- Astrophysical Institute Potsdam
- University of Basel
- Cambridge University
- Case Western Reserve University
- University of Chicago, Drexel University, Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University
- Joint Institute for Nuclear Astrophysics
- Kavli Institute for Particle Astrophysics and Cosmology
- Korean Scientist Group
- Chinese Academy of Sciences (LAMOST)
- Los Alamos National Laboratory
- Max-Planck Institute for Astronomy (MPIA)
- Max-Planck-Institute for Astrophysics (MPA)
- New Mexico State University
- Ohio State University
- University of Pittsburgh
- University of Portsmouth
- Princeton University
- United States Naval Observatory
- University of Washington
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0707059] Funding Source: National Science Foundation
Ask authors/readers for more resources
Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work, we apply a recently developed technique, diffusion maps, to astronomical spectra for data parameterization and dimensionality reduction, and develop a robust, eigenmode-based framework for regression. We show how our framework provides a computationally efficient means by which to predict redshifts of galaxies, and thus could inform more expensive redshift estimators such as template cross-correlation. It also provides a natural means by which to identify outliers (e.g., misclassified spectra, spectra with anomalous features). We analyze 3835 Sloan Digital Sky Survey spectra and show how our framework yields a more than 95% reduction in dimensionality. Finally, we show that the prediction error of the diffusion-map-based regression approach is markedly smaller than that of a similar approach based on PCA, clearly demonstrating the superiority of diffusion maps over PCA for this regression task.
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