Gradient Pattern Analysis Applied to Galaxy Morphology

Abstract

Gradient pattern analysis (GPA) is a well-established technique for measuring gradient bilateral asymmetries of a square numerical lattice. This paper introduces an improved version of GPA designed for galaxy morphometry. We show the performance of the new method on a selected sample of 54,896 objects from the SDSS-DR7 in common with Galaxy Zoo 1 catalog. The results suggest that the second gradient moment, G2, has the potential to dramatically improve over more conventional morphometric parameters. It separates early from late type galaxies better (\sim 90\%) than the CAS system (C \sim 79\%, A \sim 50\%, S \sim 43\%) and a benchmark test shows that it is applicable to hundreds of thousands of galaxies using typical processing systems.

arXiv comment5 pages, 4 figures. Accepted for publication in MNRAS

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• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
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• A New Non-Parametric Approach to Galaxy Morphological Classification
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• Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study
• Quenching, bursting and galaxy shapes: colour transformation as a function of morphology
• Unveiling Galaxy Morphology through an Unsupervised-Supervised Hybrid Approach
• Classification and Evolution of Galaxies According to the Dynamical State of Host Clusters and Galaxy Luminosities
• Galaxy Morphological Classification with Manifold Learning
• An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model
• Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction
• Morphologies for DECaLS Galaxies through a combination of non-parametric indices and machine learning methods: A comprehensive catalog using the Galaxy Morphology Extractor (galmex) code