tokens: The Gini coefficient is strongly dependent on the noise level of imaging data, and suffers from large uncertainties when signal-to-noise ratio (S/N) is not sufficiently high (Lisker 2008 ). For asymmetry A , noise contribution is estimated by measuring A on a nearby blank sky region with the same aperture. However, the noise correction for A overestimates the real noise contribution (see Figure 1 of Shi et al. 2009 ). Here, we present a detailed analysis on the noise effects in quantifying A , and introduce a revised correction for noise contamination. We construct a sample of model galaxies to test the morphological parameter A . The sample consists of 569 isolated galaxies from Wen et al. ( 2014 ) (see Section 3.2 for more details about model generation). An artificial image is obtained through combining a model galaxy image with a background noise image. The parameter A is measured from both the artificial image and the noise-free model image, following the definition given in Conselice ( 2003 ) aswhere I 0 is the intensity of the galaxy and I 180 is the -rotated intensity. The second term refers to the correction for noise contamination. A group of pixels are randomly selected from the background regions to form the background noise image. In the measurement, an aperture of 1.5 times the circular Petrosian aperture is adopted to estimate the noise contamination. Figure 12 presents comparison of the measure of A from noise-added and noise-free images for the 569 model galaxies. We find that noise contamination leads A to be systematically overestimated, and the significant scatter indicates the uncertainties of noise contribution—about 20% for a high-S/N galaxy, and up to 50% for a low-S/N galaxy (Shi et al. 2009 ). The noise correction introduced in Conselice ( 2003 ) overestimates the noise contamination and biases A to be smaller. This is because all background pixels within the Petrosian aperture are used to calculate the noise contamination. As discussed in next section, however, we argue that noise contamination is correlated with the number fraction of noise dominated pixels of a galaxy, indicating that only part of the noise contribution estimated by Equation ( 6 ) is real. Furthermore, the measured A from noise-added images is found to be lower than that from noise-free model images, in a few cases, even if no noise correction is adopted. This is because that noise dramatically changes the segmentation maps of the model galaxies. In our measurements, only pixels within 1.5 times the circular Petrosian aperture are counted to calculate A , and the extended structures of the model galaxies out of the aperture are ignored, leading to an underestimation in A . For a noise-free model galaxy image, all pixels associated with the galaxy are used to compute A . Figure 12. Comparison of asymmetry measurements from galaxy images, with and without noise. Asymmetry of the noise-free model image is marked as . For galaxy image with
ner_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O