Imfit: A Fast, Flexible New Program for Astronomical Image Fitting

TL;DR

Imfit is a new open-source, fast, and flexible astronomical image-fitting program designed primarily for galaxy analysis, featuring an object-oriented design, multiple fitting algorithms, and support for various image functions and statistical methods.

Contribution

It introduces a highly extensible, object-oriented software for galaxy image fitting with multiple algorithms and statistical options, addressing biases in low-S/N data fitting.

Findings

Poisson-based statistics reduce biases in low-S/N galaxy image fitting.

Imfit supports diverse galaxy components and models.

The program is fast, flexible, and easily extensible.

Abstract

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 which allows new types of image components (2D 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 which perform line-of-sight integration through 3D 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-S/N 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.