Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T04:35:07.705Z Has data issue: false hasContentIssue false

Connection between dynamically derived IMF normalisation and stellar populations

Published online by Cambridge University Press:  10 April 2015

Richard M. McDermid*
Affiliation:
Department of Physics and Astronomy, Macquarie University, Sydney NSW 2109, Australia Australian Astronomical Observatory, PO Box 915, Sydney NSW 1670, 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.

In this contributed talk I present recent results on the connection between stellar population properties and the normalisation of the stellar initial mass function (IMF) measured using stellar dynamics, based on a large sample of 260 early-type galaxies observed as part of the ATLAS3D project. This measure of the IMF normalisation is found to vary non-uniformly with age- and metallicity-sensitive absorption line strengths. Applying single stellar population models, there are weak but measurable trends of the IMF with age and abundance ratio. Accounting for the dependence of stellar population parameters on velocity dispersion effectively removes these trends, but subsequently introduces a trend with metallicity, such that ‘heavy’ IMFs favour lower metallicities. The correlations are weaker than those found from previous studies directly detecting low-mass stars, suggesting some degree of tension between the different approaches of measuring the IMF. Resolving these discrepancies will be the focus of future work.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2015 

References

Auger, M. W., Treu, T., Bolton, A. S., et al. 2010, ApJ, 724, 511CrossRefGoogle Scholar
Cappellari, M. & Emsellem, E. 2004, PASP, 116, 138Google Scholar
Cappellari, M., McDermid, R. M., Alatalo, K., et al. 2012, Nature, 484, 485Google Scholar
Cappellari, M., Scott, N., Alatalo, K., et al. 2013, MNRAS, 432, 1709Google Scholar
Cappellari, M., McDermid, R. M., Alatalo, K., et al. 2013, MNRAS, 432, 1862Google Scholar
Conroy, C. & van Dokkum, P. G. 2012, ApJ, 760, 71CrossRefGoogle Scholar
Conroy, C., Dutton, A. A., Graves, G. J., et al. 2013, ApJL, 776, L26CrossRefGoogle Scholar
Ferreras, I., La Barbera, F., de la Rosa, I. G., et al. 2013, MNRAS, 429, L15Google Scholar
Graves, G. J., Faber, S. M., & Schiavon, R. P. 2009b, ApJ, 698, 1590CrossRefGoogle Scholar
Kuntschner, H., Emsellem, E., Bacon, R., et al. 2010, MNRAS, 408, 97Google Scholar
La Barbera, F., Ferreras, I., Vazdekis, A., et al. 2013, MNRAS, 433, 3017Google Scholar
McDermid, R. M., Cappellari, M., Alatalo, K., et al. 2014, ApJL, 792, LL37Google Scholar
McDermid, R. M., Alatalo, K., Blitz, L., et al. 2015, MNRAS, in press. arXiv:1501.03723Google Scholar
Salpeter, E. E. 1955, ApJ, 121, 161Google Scholar
Schiavon, R. P. 2007, ApJS, 171, 146Google Scholar
Smith, R. J. 2014, MNRAS, 443, L69CrossRefGoogle Scholar
Spiniello, C., Trager, S. C., Koopmans, L. V. E., & Chen, Y. P. 2012, ApJL, 753, L32Google Scholar
Spiniello, C., Trager, S., Koopmans, L. V. E., & Conroy, C. 2014, MNRAS, 438, 1483Google Scholar
Thomas, D., Maraston, C., Bender, R. & Mendes de Oliveira, C. 2005, ApJ, 621, 673Google Scholar
Tortora, C., Romanowsky, A. J., & Napolitano, N. R. 2013, ApJ, 765, 8CrossRefGoogle Scholar
van Dokkum, P. G. & Conroy, C. 2010, Nature, 468, 940CrossRefGoogle Scholar
van Dokkum, P. G. & Conroy, C. 2011, ApJL, 735, L13Google Scholar
Vazdekis, A., Ricciardelli, E., Cenarro, A. J., et al. 2012, MNRAS, 424, 157Google Scholar