Beyond the Hubble Sequence - Exploring Galaxy Morphology with Unsupervised Machine Learning

Cheng, Ting-Yun; Huertas-Company, Marc; Conselice, Christopher J.; Arag�n-Salamanca, Alfonso; Robertson, Brant E.; Ramachandra, Nesar

Abstract

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This setup provides 27 clusters created with this unsupervised learning which we show are well separated based on galaxy shape and structure (e.g., Sersic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour-magnitude diagram, and span the range of scaling relations such as mass vs. size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of ? 87% is reached using an imbalanced dataset, matching real galaxy distributions, which includes 22.7% early-type galaxies and 77.3% late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.

Citation

Cheng, T., Huertas-Company, M., Conselice, C. J., Aragón-Salamanca, A., Robertson, B. E., & Ramachandra, N. (2021). Beyond the Hubble Sequence - Exploring Galaxy Morphology with Unsupervised Machine Learning. Monthly Notices of the Royal Astronomical Society, 503(3), 4446-4465. https://doi.org/10.1093/mnras/stab734

• Journal Article Type: Article
• Acceptance Date: Mar 8, 2021
• Online Publication Date: Mar 11, 2021
• Publication Date: May 1, 2021
• Deposit Date: Mar 23, 2021
• Publicly Available Date: Mar 23, 2021
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Oxford University Press (OUP)
• Peer Reviewed: Peer Reviewed
• Volume: 503
• Issue: 3
• Pages: 4446-4465
• DOI: https://doi.org/10.1093/mnras/stab734
• Keywords: Space and Planetary Science; Astronomy and Astrophysics
• Public URL: https://nottingham-repository.worktribe.com/output/5412166
• Publisher URL: https://academic.oup.com/mnras/article-abstract/503/3/4446/6168393?redirectedFrom=fulltext
• Additional Information: This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record Ting-Yun Cheng, Marc Huertas-Company, Christopher J Conselice, Alfonso Aragón-Salamanca, Brant E Robertson, Nesar Ramachandra, Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning, Monthly Notices of the Royal Astronomical Society, Volume 503, Issue 3, May 2021, Pages 4446–4465, is available online at: https://doi.org/10.1093/mnras/stab734.