Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-25T13:00:02.566Z Has data issue: false hasContentIssue false

The S4G view of stellar mass, mid-IR dust, and evolved, intermediate-age stars in nearby galaxies

Published online by Cambridge University Press:  17 August 2012

Sharon E. Meidt
Affiliation:
Max Planck Institute für AstronomieKönigstuhl 17, DE-69117, Heidelberg, Germany email: meidt@mpia.de
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.

With deep imaging at 3.6 and 4.5 μm where the light in nearby galaxies is dominated by old stars, the Spitzer Survey of Stellar Structure in Nearby Galaxies (S4G) promises to be the ultimate inventory of stellar mass and structure in the local universe. We present results from a novel technique that makes it possible to fully exploit the information contained in these images, pertaining not only to the stellar light (and, ultimately, mass distribution), but also the nature and distribution of the mid-IR dust and the properties of evolved, intermediate age stars (e.g. in AGB-dominated star clusters). We apply Independent Component Analysis (ICA) to the 3.6 and 4.5 μm bands to separate the light from the old stars from the secondary non-stellar (i.e. PAH and hot dust) sources of emission, which are identified via comparison to the non-stellar emission imaged at 8 μm. Then, within the context of age and mass estimation at high z, we extract optical-to-mid-IR SEDs for a sample of ICA-identified AGB-dominated clusters to constrain the typically uncertain fractional contribution of AGB light to the total stellar emission in (rest-frame) NIR bands.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2012

References

Boyer, M., Srinivasan, S., van Loon, J. et al. 2011, AJ, 142, 103Google Scholar
Bruzual, G. & Charlot, S. 2003, MNRAS, 344, 1000Google Scholar
Corbelli, E., Giovanardi, C., Palla, F., & Verley, S. 2011, A&A, 528, 116Google Scholar
Flagey, N., Boulanger, F., Verstraete, L., et al. 2006, A&A, 453, 969Google Scholar
Groenewegen, M. A. T. 2006, A&A, 448, 181Google Scholar
Grosbol, P. & Dottori, H. 2011, astro-ph/1109.4255Google Scholar
Hunter, D. A., Elmegreen, B. G., & Martin, E. 2006, AJ, 132, 801Google Scholar
Hyvärinen, A., 1999, IEEE Signal Processing Lett., 6, 145Google Scholar
Hyvärinen, A. & Oja, E. 2000, Neural Networks, 13, 411Google Scholar
Kennicutt, R. C. Jr., et al. 2003, PASP, 115, 928Google Scholar
Meidt, S. et al. 2011a, ApJ in pressGoogle Scholar
Meidt, S. et al. 2011b, in prep.Google Scholar
Mentuch, E., Abraham, R. G., Glazebrook, K., et al. 2009, ApJ, 706, 1020Google Scholar
Mentuch, E., Abraham, R. G., Zibetti, S. 2010, ApJ, 725, 1971Google Scholar
Pahre, M. A., Ashby, M. L. N., Fazio, G. G. & Willner, S. P. 2004a, ApJS, 154, 229Google Scholar
Sheth, K., Regan, M., Hinz, J., et al. 2010, PASP, 122, 1397Google Scholar