Gradient Pattern Analysis Applied to Galaxy Morphology
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with g...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.