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

TL;DR

This paper presents a probabilistic clustering algorithm for classifying galaxy morphologies in large sky surveys, demonstrating its effectiveness on SDSS data and emphasizing the importance of probabilistic methods for objective, automated galaxy classification.

Contribution

The paper introduces a new probabilistic clustering algorithm that automatically identifies optimal galaxy classes without prior assumptions, improving automated classification in astronomy.

Findings

The algorithm reliably detects meaningful galaxy classes.

It distinguishes classification from parametrisation of morphologies.

Demonstrated successful application on SDSS galaxy sample.

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.