Galaxy morphology - an unsupervised machine learning approach

TL;DR

This paper presents an unsupervised machine learning approach that automatically analyzes galaxy morphologies, producing a sequence consistent with established classification systems without human intervention.

Contribution

The study introduces a novel unsupervised computer vision method for galaxy morphology classification that aligns with traditional schemes and is scalable to large datasets.

Findings

The algorithm's morphological sequence agrees with the De Vaucouleurs system.

The method automatically deduces galaxy similarities without human input.

Source code and protocol are publicly available for replication.

Abstract

Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being analyzed. The source code of the method is publicly available, and the protocol of the experiment is included in the paper so that the experiment can be replicated, and the method can be used to analyze user-defined datasets of galaxy images.