Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-04T19:17:00.191Z Has data issue: false hasContentIssue false
  • English
  • Français

Breaking the age–metallicity degeneracy: The metallicity distribution and star formation history of the Large Magellanic Cloud

Published online by Cambridge University Press:  01 July 2008

Andrew A. Cole ,
Aaron J. Grocholski ,
Doug Geisler ,
Ata Sarajedini ,
Verne V. Smith  and
Eline Tolstoy
Andrew A. Cole
Affiliation:
School of Maths & Physics, University of Tasmania, Private Bag 37, Hobart, Tasmania 7005, Australia, email: [email protected]
Aaron J. Grocholski
Affiliation:
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD, 21218USA
Doug Geisler
Affiliation:
Dept. of Astronomy, University of Florida, P.O.Box 112055, Gainesville, FL, 32611USA
Ata Sarajedini
Affiliation:
Departamento de Fisica, Universidad de Concepción, Casilla 160-C, Concepción, Chile
Verne V. Smith
Affiliation:
Gemini Project, NOAO, Tucson, AZ, 85719USA
Eline Tolstoy
Affiliation:
Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700AV Groningen, The Netherlands
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 obtained metallicities from near-infrared calcium triplet spectroscopy for nearly a thousand red giants in 28 fields spanning a range of radial distances from the center of the bar to near the tidal radius. We have used these data to investigate the radius-metallicity and age-metallicity relations. A powerful application of these data is in conjunction with the analysis of deep HST color–magnitude diagrams (CMDs). Most of the power in determining a robust star-formation history from a CMD comes from the main-sequence turnoff and subgiant branches. The age-metallicity degeneracy that results is largely broken by the red giant branch color, but theoretical model RGB colors remain uncertain. By incorporating the observed metallicity distribution function into the modelling process, a star-formation history with massively increased precision and accuracy can be derived. We incorporate the observed metallicity distribution of the LMC bar into a maximum-likelihood analysis of the bar CMD, and present a new star formation history and age–metallicity relation for the bar. The bar is certainly younger than the disk as a whole, and the most reliable estimates of its age are in the 5–6 Gyr range, when the mean gas abundance of the LMC had already increased to [Fe/H] ≳ −0.6. There is no obvious metallicity gradient among the old stars in the LMC disk out to a distance of 8–10 kpc, but the bar is more metal-rich than the disk by ≈0.1–0.2 dex. This is likely to be the result of the bar's younger average age. In both disk and bar, 95% of the red giants are more metal-rich than [Fe/H] = −1.2.

Keywords

techniques: spectroscopicstars: abundancesstars: evolutiongalaxies: abundancesgalaxies: evolutiongalaxies: individual (LMC)Magellanic Cloudsgalaxies: stellar contentgalaxies: structure
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 4 , Symposium S256: The Magellanic System: Stars, Gas, and Galaxies , July 2008 , pp. 263 - 268
Copyright
Copyright © International Astronomical Union 2009

References

Butcher, H. 1977, ApJ, 216, 372CrossRefGoogle Scholar
Carrera, R., Gallart, C., Hardy, E., Aparicio, A., & Zinn, R. 2008, AJ, 135, 836CrossRefGoogle Scholar
Cole, A. A., Smecker-Hane, T. A., & Gallagher, J. S. 2000, AJ, 120, 1808CrossRefGoogle Scholar
Cole, A. A., Smecker-Hane, T. A., Tolstoy, E., Bosler, T. L., & Gallagher, J. S. 2004, MNRAS, 347, 367CrossRefGoogle Scholar
Cole, A. A., Gallagher, J. S., Tolstoy, E., & Smecker-Hane, T. A. 2005, AJ, 129, 1465CrossRefGoogle Scholar
Da Costa, G. S. & Hatzidimitriou, D. 1998, AJ, 115, 1934CrossRefGoogle Scholar
Gallart, C., Zoccali, M., & Aparicio, A. 2005, ARAA, 43, 387CrossRefGoogle Scholar
Grocholski, A. J., Cole, A. A., Sarajedini, A., Geisler, D., & Smith, V. V. 2006, AJ, 132, 1630CrossRefGoogle Scholar
Holtzman, J. A., Gallagher, J. S. III, Cole, A. A., et al. 1999, AJ, 118, 2262CrossRefGoogle Scholar
Kallivayalil, N., van der Marel, R. P., Alcock, C., et al. 2006, ApJ, 638, 772CrossRefGoogle Scholar
Kallivayalil, N., van der Marel, R. P., & Alcock, C. 2006, ApJ, 652, 1213CrossRefGoogle Scholar
Olszewski, E. W. 1993, ASPC, 48, 351Google Scholar
Olszewski, E. W., Schommer, R. A., Suntzeff, N. B., & Harris, H. C. 1991, AJ, 101, 515CrossRefGoogle Scholar
Pagel, B. E. J. & Tautvaišienė, G. 1998, MNRAS, 299, 535Google Scholar
Pompéia, L., Hill, V., Spite, M., et al. 2008, A&A, 380, 379Google Scholar
Rutledge, G. A., Hesser, J. E., & Stetson, P. B. 1997, PASP, 109, 883CrossRefGoogle Scholar
Salaris, M. 2002, ASPC, 274, 50Google Scholar
Skillman, E. D. & Gallart, C. 2002, ASPC, 274, 535Google Scholar
Smecker-Hane, T. A., Cole, A. A., Gallagher, J. S., & Stetson, P. B. 2002, ApJ, 566, 239CrossRefGoogle Scholar
Smith, V. V., Hinkle, K. H., Cunha, K., et al. 2002, AJ, 124, 3241CrossRefGoogle Scholar
van der Marel, R. P. 2001, AJ, 122, 1827CrossRefGoogle Scholar
Worthey, G. 1999, ASPC, 192, 283Google Scholar