Is the Gini coefficient a stable measure of galaxy structure?

TL;DR

This paper critically evaluates the stability of the Gini coefficient as a measure of galaxy structure, revealing its dependence on signal-to-noise ratio and measurement aperture, which limits its reliability in galaxy morphology studies.

Contribution

It provides a systematic assessment of the Gini coefficient's stability across different data qualities and measurement choices, establishing conditions for its reliable use.

Findings

Gini coefficient depends strongly on signal-to-noise ratio below a certain threshold.

Measurement aperture significantly influences Gini coefficient values.

Reliable Gini measurements require signal-to-noise ratios above established limits.

Abstract

The Gini coefficient, a non-parametric measure of galaxy morphology, has recently taken up an important role in the automated identification of galaxy mergers. I present a critical assessment of its stability, based on a comparison of HST/ACS imaging data from the GOODS and UDF surveys. Below a certain signal-to-noise level, the Gini coefficient depends strongly on the signal-to-noise ratio, and thus becomes useless for distinguishing different galaxy morphologies. Moreover, at all signal-to-noise levels the Gini coefficient shows a strong dependence on the choice of aperture within which it is measured. Consequently, quantitative selection criteria involving the Gini coefficient, such as a selection of merger candidates, cannot always be straightforwardly applied to different datasets. I discuss whether these effects could have affected previous studies that were based on the Gini coefficient, and establish signal-to-noise limits above which measured Gini values can be considered reliable.