Reliable Eigenspectra for New Generation Surveys
arXiv:0809.0881 · doi:10.1111/j.1365-2966.2009.14415.x
Abstract
We present a novel technique to overcome the limitations of the applicability of Principal Component Analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step-by-step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilising all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the VIMOS VLT Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond.
7 pages, 3 figures, accepted to MNRAS
References in corpus (10)
- Differential Galaxy Evolution in Cluster and Field Galaxies at z=0.3
- Spectral Classification of Quasars in the Sloan Digital Sky Survey: Eigenspectra; Redshift and Luminosity Effects
- Spectral Classification of Galaxies: An Orthogonal Approach
- Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey
- Bursty stellar populations and obscured AGN in galaxy bulges
- The VIRMOS deep imaging survey II: CFH12K BVRI optical data for the 0226-04 deep field
- Spectral Templates from Multicolor Redshift Surveys
- Creating Spectral Templates from Multicolor Redshift Surveys
- Automatic Redshift Determination by use of Principal Component Analysis --- I: Fundamentals
- A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data
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