INTEGRATED STELLAR POPULATIONS: CONFRONTING PHOTOMETRY WITH SPECTROSCOPY
- Herzberg Institute of Astrophysics, National Research Council of Canada, 5071 West Saanich Road, Victoria, BC V8X 4M6 (Canada)
- Department of Astronomy, University of Maryland, College Park, MD 20742-2421 (United States)
- Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ON K7L 3N6 (Canada)
We investigate the ability of spectroscopic techniques to yield realistic star formation histories (SFHs) for the bulges of spiral galaxies based on a comparison with their observed broadband colors. Full spectrum fitting to optical spectra indicates that recent (within {approx}1 Gyr) star formation activity can contribute significantly to the V-band flux, whilst accounting for only a minor fraction of the stellar mass budget which is made up primarily of old stars. Furthermore, recent implementations of stellar population (SP) models reveal that the inclusion of a more complete treatment of the thermally pulsating asymptotic giant branch (TP-AGB) phase to SP models greatly increases the NIR flux for SPs of ages 0.2-2 Gyr. Comparing the optical-NIR colors predicted from population synthesis fitting, using models which do not include all stages of the TP-AGB phase, to the observed colors reveals that observed optical-NIR colors are too red compared to the model predictions. However, when a 1 Gyr SP from models including a full treatment of the TP-AGB phase is used, the observed and predicted colors are in good agreement. This has strong implications for the interpretation of stellar populations, dust content, and SFHs derived from colors alone.
- OSTI ID:
- 21455114
- Journal Information:
- Astrophysical Journal, Vol. 718, Issue 2; Other Information: DOI: 10.1088/0004-637X/718/2/768; ISSN 0004-637X
- Country of Publication:
- United States
- Language:
- English
Similar Records
GALAXIES M32 AND NGC 5102 CONFIRM A NEAR-INFRARED SPECTROSCOPIC CHRONOMETER
THE PROPAGATION OF UNCERTAINTIES IN STELLAR POPULATION SYNTHESIS MODELING. III. MODEL CALIBRATION, COMPARISON, AND EVALUATION