Soft clustering analysis of galaxy morphologies: A worked example with SDSS

Abstract

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover classes automatically. Aims: We briefly discuss the pitfalls of oversimplified classification methods and outline an alternative approach called "clustering analysis". Methods: We categorise different classification methods according to their capabilities. Based on this categorisation, we present a probabilistic classification algorithm that automatically detects the optimal classes preferred by the data. We explore the reliability of this algorithm in systematic tests. Using a small sample of bright galaxies from the SDSS, we demonstrate the performance of this algorithm in practice. We are able to disentangle the problems of classification and parametrisation of galaxy morphologies in this case. Results: We give physical arguments that a probabilistic classification scheme is necessary. The algorithm we present produces reasonable morphological classes and object-to-class assignments without any prior assumptions. Conclusions: There are sophisticated automated classification algorithms that meet all necessary requirements, but a lot of work is still needed on the interpretation of the results.

18 pages, 19 figures, 2 tables, submitted to AA

References in corpus (5)

• Galaxy Zoo: the dependence of morphology and colour on environment
• A Catalogue of Morphologically Classified Galaxies from the Sloan Digital Sky Survey: North Equatorial Region
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. II. Quantifying morphological k-correction in the COSMOS field at 1<z<2: Ks band vs. I band
• Reliable Shapelet Image Analysis

Cited by in corpus (9)

• The weirdest SDSS galaxies: results from an outlier detection algorithm
• Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
• A review of unsupervised learning in astronomy
• Multivariate Approaches to Classification in Extragalactic Astronomy
• Parametrising arbitrary galaxy morphologies: potentials and pitfalls
• Only marginal alignment of disc galaxies
• Quantifying galaxy shapes: Sersiclets and beyond
• Galaxy Modeling with Compound Elliptical Shapelets
• Where's Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data