Bayesian fitting of multi-Gaussian expansion models to galaxy images

Abstract

Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward modelling technique allows one to account for the PSF to effectively study semi-resolved galaxies. However, using a specific parameterization for a galaxy's surface brightness profile can bias measurements if it is not an accurate representation. Furthermore, it can be difficult to assess systematic errors in parameterized profiles. To overcome these issues we employ the Multi-Gaussian expansion (MGE) method of representing a galaxy's profile together with a Bayesian framework for fitting images. MGE flexibly represents a galaxy's profile using a series of Gaussians. We introduce a novel Bayesian inference approach which uses pre-rendered Gaussian components, which greatly speeds up computation time and makes it feasible to run the fitting code on large samples of galaxies. We demonstrate our method with a series of validation tests. By injecting galaxies, with properties similar to those observed at z ∼ 1.5, into deep HST observations we show that it can accurately recover total fluxes and effective radii of realistic galaxies. Additionally we use degraded images of local galaxies to show that our method can recover realistic galaxy surface brightness and color profiles. Our implementation is available in an open source python package imcascade, which contains all methods needed for the preparation of images, fitting and analysis of results.

Reaching for the Edge I: Probing the Outskirts of Massive Galaxies with HSC, DECaLS, SDSS, and Dragonfly

“…The Dragonfly Telephoto Array has already shown tremendous potential for detecting extended low surface brightness phenomena (van Dokkum et al 2014;Merritt et al 2016a,b;Zhang et al 2018;Cohen et al 2018;Miller et al 2021;Keim et al 2021). However, Dragonfly has the worst seeing (FWHM ∼ 5 arcsec) and lowest resolution (2.5 per pixel) among the four surveys, which makes blending a major issue in Dragonfly data.…”

“…The algorithm also models halos around bright stars by stacking bright stars. MRF has already shown its potential in Dragonfly data (van Dokkum et al 2019b;Gilhuly et al 2019;Danieli et al 2020;Miller et al 2021;Keim et al 2021). We apply MRF to the Dragonfly images, mask out residual pixels, and extract 1-D surface brightness profiles using the same methodology as in Section 3.1.1.…”

“…Here choices involve either using 2-D model fitting or 1-D surface brightness profile analyses. In principle, the 2-D fitting approach has several key advantages (e.g., Gonzalez et al 2005;Huang et al 2013;Miller & van Dokkum 2021). Model fitting takes the PSF convolution into account and it is straightforward to integrate the results to get the total luminosity.…”

“…First, they are needed for correct estimation of total stellar mass (e.g. Bernardi et al 2013;Miller et al 2021). Second, at large radii, the long dynamical time means that fossil evidence of the assembly history may be preserved (e.g.…”

“…The goal of this research is to study how far in radius current surveys and techniques can reliably measure the outer regions of massive galaxies. We focus on four independent data sets that have been used in various analysis of massive galaxies and for studies of LSB features: the Hyper Suprime-Cam Subaru Strategic Program Survey (HSC, Aihara et al 2018), which offers deep images with good seeing but limited sky coverage; the Dark Energy Camera Legacy Survey (DECaLS, Dey et al 2019), with wide sky coverage, deeper than SDSS but shallower than HSC; the Dragonfly Wide Field Survey (Dragonfly, Danieli et al 2020;Miller et al 2021), which is extremely deep and optimized for LSB detection but has poor spatial resolution and small sky coverage cur-rently; and the Sloan Digital Sky Survey (SDSS, Abazajian et al 2009), which is shallow, has very large sky coverage and has been used in many different studies. We compare the surface brightness profiles extracted from these four surveys as a means of empirically determining the robustness of outer light profile measurements for massive galaxies.…”