Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T17:16:29.909Z Has data issue: false hasContentIssue false
  • English
  • Français

Maximum-likelihood implementation of the tip-of-the-red-giant-branch method

Published online by Cambridge University Press:  26 February 2013

Dmitry Makarov
Dmitry Makarov*
Affiliation:
Special Astrophysical Observatory, Russian Academy of Sciences, N. Arkhyz, KChR, 369167, Russia 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.

The luminosity of the tip of the red giant branch (TRGB) provides an excellent measure of galaxy distances and is easily determined in the resolved images of nearby galaxies observed with the Hubble Space Telescope (HST). We use a maximum-likelihood algorithm to locate the TRGB in galaxy colour–magnitude diagrams. The algorithm is optimized by introducing reliable photometric errors and a completeness characterization determined based on artificial-star experiments. The program has been tested extensively using Monte Carlo simulations, artificial galaxies, and a sample of nearby dwarf galaxies observed with the HST's WFPC2 and ACS cameras. Our procedure is shown to be reliable, yield good accuracy, and does not introduce any systematic errors. The methodology is especially useful in cases in which the TRGB approaches the photometric limit and/or the RGB is poorly populated.

Keywords

methods: data analysisgalaxies: distances and redshifts
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 8 , Symposium S289: Advancing the Physics of Cosmic Distances , August 2012 , pp. 218 - 221
Copyright
Copyright © International Astronomical Union 2013

References

Bellazzini, M., Ferraro, F., & Pancino, E. 2001, ApJ, 556, 635CrossRefGoogle Scholar
Dolphin, A. E. 2002, MNRAS, 332, 91Google Scholar
Lee, M. G., Freedman, W. L., & Madore, B. F. 1993, ApJ, 417, 553CrossRefGoogle Scholar
Makarov, D., Makarova, L., Rizzi, L., Tully, R. B., Dolphin, A. E., Sakai, S., & Shaya, E. J. 2006, AJ, 132, 2729Google Scholar
Makarova, L., Makarov, D., & Savchenko, S. 2012, MNRAS, 423, 294Google Scholar
Méndez, B., Davis, M., Moustakas, J., Newman, J., Madore, B. F., & Freedman, W. L. 2002, AJ, 124, 213Google Scholar
Rizzi, L., Tully, R. B., Makarov, D., Makarova, L., Dolphin, A. E., Sakai, S., & Shaya, E. J. 2007, ApJ, 661, 815Google Scholar
Sakai, S., Madore, B. F., & Freedman, W. L. 1996, ApJ, 461, 713CrossRefGoogle Scholar