Journal
ASTROPHYSICAL JOURNAL
Volume 799, Issue 2, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/0004-637X/799/2/226
Keywords
galaxies: bulges; galaxies: photometry; galaxies: structure; methods: data analysis; techniques: image processing; techniques: photometric
Categories
Funding
- Alfred P. Sloan Foundation
- National Aeronautics and Space Administration
- National Science Foundation
- U.S. Department of Energy
- Japanese Monbukagakusho
- Max Planck Society
- University of Chicago
- Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University
- Korean Scientist Group
- Los Alamos National Laboratory
- Max-Planck-Institute for Astronomy (MPIA)
- Max-Planck-Institute for Astrophysics (MPA)
- New Mexico State University
- University of Pittsburgh
- University of Portsmouth
- Princeton University
- United States Naval Observatory
- University of Washington
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I describe a new, open-source astronomical image-fitting program called IMFIT, specialized for galaxies but potentially useful for other sources, which is fast, flexible, and highly extensible. A key characteristic of the program is an object-oriented design that allows new types of image components (two-dimensional surface-brightness functions) to be easily written and added to the program. Image functions provided with imfit include the usual suspects for galaxy decompositions (Sersic, exponential, Gaussian), along with Core-Sersic and broken-exponential profiles, elliptical rings, and three components that perform line-of-sight integration through three-dimensional luminosity-density models of disks and rings seen at arbitrary inclinations. Available minimization algorithms include Levenberg-Marquardt, Nelder-Mead simplex, and Differential Evolution, allowing trade-offs between speed and decreased sensitivity to local minima in the fit landscape. Minimization can be done using the standard chi(2) statistic (using either data or model values to estimate per-pixel Gaussian errors, or else user-supplied error images) or Poisson-based maximum-likelihood statistics; the latter approach is particularly appropriate for cases of Poisson data in the low-count regime. I show that fitting low-signal-to-noise ratio galaxy images using chi(2) minimization and individual-pixel Gaussian uncertainties can lead to significant biases in fitted parameter values, which are avoided if a Poisson-based statistic is used; this is true even when Gaussian read noise is present.
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