Gradient Pattern Analysis Applied to Galaxy Morphology

TL;DR

This paper presents an improved gradient pattern analysis method for galaxy morphology classification, demonstrating superior accuracy in distinguishing galaxy types compared to traditional parameters on a large SDSS dataset.

Contribution

An enhanced GPA technique tailored for galaxy morphometry, showing significant improvement in galaxy classification accuracy over conventional methods.

Findings

G2 parameter separates early and late type galaxies with ~90% accuracy

The method outperforms the CAS system in galaxy classification

Applicable to large datasets with typical processing systems

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.