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Advances in Astronomy
Volume 2012 (2012), Article ID 208901, 11 pages
http://dx.doi.org/10.1155/2012/208901
Research Article

A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample

1Department of Physics and Graduate Institute of Astrophysics, National Taiwan University, Taipei 10617, Taiwan
2Leung Center for Cosmology and Particle Astrophysics, National Taiwan University, Taipei 10617, Taiwan
3Institute of Astronomy and Astrophysics, Academia Sinica, Taipei 10617, Taiwan
4Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan
5Kavli Institute for Particle Astrophysics and Cosmology, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305, USA

Received 11 January 2012; Revised 17 March 2012; Accepted 19 March 2012

Academic Editor: Gary Wegner

Copyright © 2012 Yu-Yen Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Disney et al. (2008) have found a striking correlation among global parameters of Hi-selected galaxies and concluded that this is in conflict with the CDM model. Considering the importance of the issue, we reinvestigate the problem using the principal component analysis on a fivefold larger sample and additional near-infrared data. We use databases from the Arecibo Legacy Fast Arecibo L-band Feed Array Survey for the gas properties, the Sloan Digital Sky Survey for the optical properties, and the Two Micron All Sky Survey for the near-infrared properties. We confirm that the parameters are indeed correlated where a single physical parameter can explain 83% of the variations. When color (g-i) is included, the first component still dominates but it develops a second principal component. In addition, the near-infrared color (i-J) shows an obvious second principal component that might provide evidence of the complex old star formation. Based on our data, we suggest that it is premature to pronounce the failure of the CDM model and it motivates more theoretical work.