Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-25T01:42:21.606Z Has data issue: false hasContentIssue false
  • English
  • Français

DEEP, AEGIS, & CATS - Pathfinding Surveys to the Next Generation of Distant Galaxy Stellar Population Research

Published online by Cambridge University Press:  13 April 2010

David C. Koo
David C. Koo
Affiliation:
UCO/Lick Observatory, Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

DEEP, AEGIS, and CATS are examples of spectroscopic, multiwavelength, and adaptive optics surveys, respectively, that are pushing the frontiers of stellar population studies of distant galaxies. Together, these surveys affirm the unprecedented richness of high quality information that can already be gathered using today's generation of ground and space telescopes. We highlight several results in extracting the stellar and dynamical masses, chemical abundances, ages, and frequency of galactic winds for galaxies at redshifts up to z ~ 1.4.

Keywords

surveysgalaxies: evolutiongalaxies: high redshiftgalaxies: kinematicsinstrumentation: adaptive optics
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 5 , Symposium S262: Stellar Populations – Planning for the Next Decade , August 2009 , pp. 176 - 183
Copyright
Copyright © International Astronomical Union 2010

References

Ammons, A. M. et al. 2009, AJ, 137, 470CrossRefGoogle Scholar
Conselice, C. J. et al. 2005, ApJ, 628, 160CrossRefGoogle Scholar
Covington, M. et al. 2010, ApJ, in pressGoogle Scholar
Davis, M. et al. 2007, ApJ, 660, L1CrossRefGoogle Scholar
Erb, D. et al. 2006, ApJ, 644, 813CrossRefGoogle Scholar
Flores, H. et al. 2006, AA, 455, 107CrossRefGoogle Scholar
Glassman, T. M. et al. 2002, ApJ, 581, 865CrossRefGoogle Scholar
Hoyos, C. et al. 2005, ApJ, 635, L21CrossRefGoogle Scholar
Hoyos, C. et al. 2010, ApJ, in preparationGoogle Scholar
Hu, E. et al. 2009, ApJ, 698, 2014CrossRefGoogle Scholar
Kakazu, Y. et al. 2007, ApJ, 668, 853CrossRefGoogle Scholar
Kassin, S. et al. 2007, ApJ, 660, L35CrossRefGoogle Scholar
Kunth, D. & Ostlin, G. 2000, A&AR, 10, 1Google Scholar
Larkin, J. E. et al. 2000, PASP, 112, 1526CrossRefGoogle Scholar
Lee, H. et al. 2006, ApJ, 647, 970CrossRefGoogle Scholar
Liang, Y. C. et al. 2006, AA, 447, 113CrossRefGoogle Scholar
Martin, C. L. 2005, ApJ, 621, 227CrossRefGoogle Scholar
Melbourne, J., et al. 2005, ApJ, 625, L27CrossRefGoogle Scholar
Melbourne, J. et al. 2008, AJ, 135, 1207CrossRefGoogle Scholar
Newman, J. A. et al. 2010, ApJ, in preparationGoogle Scholar
Savaglio, S. et al. 2005, ApJ, 635, 260CrossRefGoogle Scholar
Shapley, A. E. et al. 2005, ApJ, 635, 1006CrossRefGoogle Scholar
Steinbring, E. et al. 2004, ApJS, 155, 15CrossRefGoogle Scholar
Steinbring, E. et al. 2008, AJ, 136, 1523CrossRefGoogle Scholar
Tremonti, C. A. et al. 2004, ApJ, 613, 898CrossRefGoogle Scholar
Vogt, N. P. et al. 2005, ApJS, 159, 41CrossRefGoogle Scholar
Weiner, B. J. et al. 2009, ApJ, 692, 187CrossRefGoogle Scholar
Wizinowich, P. et al. 2006, PASP, 118, 297CrossRefGoogle Scholar