Dissertation
INVESTIGATING [X/FE], IMF AND COMPOSITENESS IN INTEGRATED MODELS
Doctor of Philosophy (PhD), Washington State University
01/2015
Handle:
https://hdl.handle.net/2376/5492
Abstract
This dissertation explores several existing challenges of evolutionary stellar population synthesis models in integrated light: age-metallicity degeneracy, initial mass function (IMF), elemental abundances, and compositeness. First, we search for age-sensitive and metal-sensitive colors in three photometric systems. We also add to the discussion of optical to near-infrared Johnson-Cousins broad band colors, finding a great decrease in age sensitivity when updated isochrones are used. Then we investigate the element abundances and compositeness of our models, in which we assume a single-peak abundance distribution and the same elemental abundance trends as the Milky Way bulge stars. Varying the width of the abundance distribution function reveals novel ``red lean'' and ``red spread'' effects. Next, we study three effects that co-determine the dwarf/giant ratio: the IMF slope, the IMF low mass cut-off (LMCO), and AGB star contributions. This degeneracy can be lifted for old, metal-rich stellar populations, although at an observationally challenging level ($\\approx$0.02 mag). Finally, we select and reduce more than 200 $z\\sim0.4$ red galaxy spectra from the DEEP2 sky survey, and measure the Lick-style spectral indices from the composite spectra. Multiple optical IMF-sensitive indices suggest a shallower IMF that qualitatively agrees with current literature.
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Details
- Title
- INVESTIGATING [X/FE], IMF AND COMPOSITENESS IN INTEGRATED MODELS
- Creators
- Baitian Tang
- Contributors
- Guy Worthey (Advisor)Sukanta Bose (Committee Member)Matthew Duez (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Physics and Astronomy, Department of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 150
- Identifiers
- 99900581840001842
- Language
- English
- Resource Type
- Dissertation