Fast Shape Estimation for Galaxies and Stars

TL;DR

This paper introduces a set of algorithms that significantly accelerate the process of fitting models to galaxy and star images, focusing on centroiding, ellipticity, and profile fitting, to facilitate large-scale astronomical surveys.

Contribution

The authors present a novel three-step algorithm that analytically derives position and ellipticity, reducing the parameters needing numerical fitting, thus speeding up shape estimation in astronomical images.

Findings

Algorithms are efficient and accurate on simulated images.

Method reduces computational complexity for large surveys.

Shape parameters can be used for improved galaxy image reconstruction.

Abstract

Model fitting is frequently used to determine the shape of galaxies and the point spread function, for examples, in weak lensing analyses or morphology studies aiming at probing the evolution of galaxies. However, the number of parameters in the model, as well as the number of objects, are often so large as to limit the use of model fitting for future large surveys. In this article, we propose a set of algorithms to speed up the fitting process. Our approach is divided into three distinctive steps: centroiding, ellipticity measurement, and profile fitting. We demonstrate that we can derive the position and ellipticity of an object analytically in the first two steps and thus leave only a small number of parameters to be derived through model fitting. The position, ellipticity, and shape parameters can then used in constructing orthonomal basis functions such as s\'ersiclets for better galaxy image reconstruction. We assess the efficiency and accuracy of the algorithms with simulated images. We have not taken into account the deconvolution of the point spread function, which most weak lensing analyses do.