New image statistics for detecting disturbed galaxy morphologies at high redshift

Abstract

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g., merging galaxies). This is often done by summarizing galaxy images using, e.g., the CAS and Gini-M 20 statistics of Conselice (2003) and Lotz et al. (2004), respectively, and associating particular statistic values with disturbance. We introduce three statistics that enhance detection of disturbed morphologies at high-redshift (z ∼ 2): the multi-mode (M ), intensity (I), and deviation (D) statistics. We show their effectiveness by training a machinelearning classifier, random forest, using 1,639 galaxies observed in the H-band by the Hubble Space Telescope WFC3, galaxies that had been previously classified by eye by the CANDELS collaboration (Grogin et al. 2011, Koekemoer et al. 2011. We find that the M ID statistics (and the A statistic of Conselice 2003) are the most useful for identifying disturbed morphologies.We also explore whether human annotators are useful for identifying disturbed morphologies. We demonstrate that they show limited ability to detect disturbance at high redshift, and that increasing their number beyond ≈10 does not provably yield better classification performance. We propose a simulation-based model-fitting algorithm that mitigates these issues by bypassing annotation.

Diverse structural evolution atz > 1 in cosmologically simulated galaxies

“…as well as three new indicators Multimode (M), Intensity (I) and Deviation (D) statistics by Freeman et al (2013). These quantities describe the light profile of observed sources in a computationally efficient manner, and have been used to classify galaxy structural types and morphological disturbances in numerous surveys, including CANDELS (Peth et al in prep.).…”

“…The M , I, and D statistics were introduced by Freeman et al (2013) to automatically identify disturbed morphologies in a way that reproduces the results of visual classifications. Let S l be a superlevel set for I, i.e., S l is the collection of pixels within a galaxy's segmentation map with intensity greater than or equal to a given threshold l. Given this collection, one groups all contiguous pixels, orders the groups by decreasing area (such that A l,(i) is the area of the i th largest group), and sets…”

“…For instance, Freeman et al (2013), who worked with HST images from the GOODS-S field, smoothed their image data with a symmetric Gaussian kernel with σ ≈ 1 pixel. The D statistic identifies galaxies whose shapes deviate from elliptical symmetry, and thus it serves a similar function to the A statistic defined above.…”

“…The M , I, and D statistics were designed to automatically identify mergers in a way that reproduces the results of visual classifications (Freeman et al 2013). The D statistic traces similar features as Asymmetry.…”

KINEMATIC CLASSIFICATIONS OF LOCAL INTERACTING GALAXIES: IMPLICATIONS FOR THE MERGER/DISK CLASSIFICATIONS AT HIGH-z

“…Numerical simulations have shown that violent encounters of gas-rich spiral galaxies can result in various observational footprints of interactions such as bridges and tidal tails (e.g., Toomre & Toomre 1972;Barnes & Hernquist 1992). While visual classification is still used for identifying mergers out to~-z 1 3 (e.g., Bell et al 2005;Dasyra et al 2008;Kartaltepe et al 2010Kartaltepe et al , 2012Hung et al 2013), various automatic classification schemes have also been developed and applied to large extragalactic surveys (e.g., Abraham et al 2003;Conselice 2003;Lotz et al 2004;Law et al 2007;Freeman et al 2013). Identifying such merger signatures with either visual or automatic classifications at~-z 1 3 is challenging due to surface brightness dimming and band-shifting (e.g., Hibbard & Vacca 1997;Petty et al 2009), which can lead to a significant underestimation of the occurrence of mergers (e.g., Abraham et al 1996;Overzier et al 2010;Hung et al 2014;Petty et al 2014).…”