Improving galaxy morphology with machine learning
Paulo Henrique Barchi, Francisca Costa, Rubens Sautter, Renata Lopes Rosa, R. R. de Carvalho · Journal of Computational Interdisciplinary Sciences · 2016
This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA).Except concentration, all parameters performed a image segmentation pre-processing.For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo.With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT).Whit a 1962 objects balanced dataset, we applied Kmeans and Agglomerative Hierarchical Clustering.All experiments with supervision reached an Overall Accuracy OA ≥ 97%.