Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-26T13:58:58.246Z Has data issue: false hasContentIssue false

The stellar populations in low excitation and high excitation radio galaxies

Published online by Cambridge University Press:  17 July 2013

Michael B. Pracy
Affiliation:
Sydney Institute for Astronomy, School of Physics, University of Sydney, NSW, 2006, Australia email: [email protected]
John Ching
Affiliation:
Sydney Institute for Astronomy, School of Physics, University of Sydney, NSW, 2006, Australia email: [email protected]
Scott Croom
Affiliation:
Sydney Institute for Astronomy, School of Physics, University of Sydney, NSW, 2006, Australia email: [email protected]
Elaine M. Sadler
Affiliation:
Sydney Institute for Astronomy, School of Physics, University of Sydney, NSW, 2006, Australia 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.

We have conducted deep optical spectroscopic follow up of a sample of radio galaxies with redshifts z < 0.7. The spectra were used to construct robust sub-samples of low excitation and high excitation AGN and perform stellar population analysis via line indices and spectral fitting. While the high excitation objects have lower luminosity-weighted ages and lower metallicities than the low excitation galaxies, this can be explained by the different stellar mass distributions of the samples. When stellar mass is taken into account the age and metallicity distribution of both populations are consistent with the galaxy population as a whole.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2013 

References

Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5CrossRefGoogle Scholar
Becker, R. H., White, R. L., & Helfand, D. H. 1995, ApJ, 450, 559Google Scholar
Best, P. N. & Heckman, T. M. 2012, MNRAS, 421, 1569Google Scholar
Ching, J., et al. 2013, in preparationGoogle Scholar
Croom, S. M., et al. 2013, in preparationGoogle Scholar
Croton, D. J., et al. 2006, MNRAS, 365, 11Google Scholar
Gallazzi, A., Charlot, S., Brinchmann, J., & White, S. D. M., Tremonti, C. 2005, MNRAS, 362, 41Google Scholar
Hardcastle, M. J., Evans, D. A., & Croston, J. H. 2007, MNRAS, 376, 1849Google Scholar
Jackson, N. & Rawlings, S. 1997, MNRAS, 286, 241CrossRefGoogle Scholar
Kauffmann, G., Heckman, T. M., & Best, P. N. 2008, MNRAS, 384, 953Google Scholar
Lamareille, F. 2010, A&A, 509, 53Google Scholar
McNamara, B. R. & Nulsen, P. E. J. 2007, ARA&A, 45, 117Google Scholar
Ogle, P., Whysong, D., & Antonucci, R. 2006, ApJ, 647, 161Google Scholar
Thomas, D., Maraston, C., & Korn, A. 2004, MNRAS, 351, L19CrossRefGoogle Scholar
Vazdekis, A., et al. 2010, MNRAS, 404, 1639Google Scholar
Whysong, D. & Antonucci, R. 2004, ApJ, 602, 116Google Scholar