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arXiv · 2106.13809

AutoProf -- I. An automated non-parametric light profile pipeline for modern galaxy surveys

Connor Stone·Nikhil Arora·Stéphane Courteau·Jean-Charles Cuillandre
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Abstract

We present an automated non-parametric light profile extraction pipeline called AutoProf. All steps for extracting surface brightness (SB) profiles are included in AutoProf, allowing streamlined analyses of galaxy images. AutoProf improves upon previous non-parametric ellipse fitting implementations with fit-stabilization procedures adapted from machine learning techniques. Additional advanced analysis methods are included in the flexible pipeline for the extraction of alternative brightness profiles (along radial or axial slices), smooth axisymmetric models, and the implementation of decision trees for arbitrarily complex pipelines. Detailed comparisons with widely used photometry algorithms (photutils, XVISTA, and GALFIT) are presented. These comparisons rely on a large collection of late-type galaxy images from the PROBES survey. The direct comparison of SB profiles shows that AutoProf can reliably extract fainter isophotes than other methods on the same images, typically by >2 mag/arcsec^2. Contrasting non-parametric elliptical isophote fitting with simple parametric models also shows that two-component fits (e.g., Sérsic plus exponential) are insufficient to describe late-type galaxies with high fidelity. It is established that elliptical isophote fitting, and in particular AutoProf, is ideally suited for a broad range of automated isophotal analysis tasks. AutoProf is freely available to the community at: https://github.com/ConnorStoneAstro/AutoProf

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Connor Stone, Nikhil Arora, Stéphane Courteau, Jean-Charles Cuillandre. 2021-10-07. AutoProf -- I. An automated non-parametric light profile pipeline for modern galaxy surveys. https://doi.org/10.1093/mnras%2Fstab2709

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