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arXiv · 1705.06818

Improving galaxy morphology with machine learning

P. H. Barchi·F. G. da Costa·R. Sautter·T. C. Moura·D. H. Stalder·R. R. Rosa·R. R. de Carvalho
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Abstract

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 K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.

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P. H. Barchi, F. G. da Costa, R. Sautter, T. C. Moura, D. H. Stalder, R. R. Rosa, R. R. de Carvalho. 2017-05-18. Improving galaxy morphology with machine learning. https://doi.org/10.6062/jcis.2016.07.03.0114

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