Unveiling Galaxy Morphology through an Unsupervised-Supervised Hybrid Approach
arXiv:2401.08906 · doi:10.1093/mnras/stad3934
Abstract
Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze the vast numbers of galaxies that will be observed. In this work, we introduce a novel classification technique blending unsupervised clustering based on morphological metrics with the scalability of supervised Convolutional Neural Networks. We delve into a comparative analysis between the well-known CAS (Concentration, Asymmetry, and Smoothness) metrics and our newly proposed EGG (Entropy, Gini, and Gradient Pattern Analysis). Our choice of the EGG system stems from its separation-oriented metrics, maximizing morphological class contrast. We leverage relationships between metrics and morphological classes, leading to an internal agreement between unsupervised clustering and supervised classification. Applying our methodology to the Sloan Digital Sky Survey data, we obtain 95% of Overall Accuracy of purely unsupervised classification and when we replicate T-Type and visually classified galaxy catalogs with accuracy of 88% and 89% respectively, illustrating the method's practicality. Furthermore, the application to Hubble Space Telescope data heralds the potential for unsupervised exploration of a higher redshift range. A notable achievement is our 95% accuracy in unsupervised classification, a result that rivals when juxtaposed with Traditional Machine Learning and closely trails when compared to Deep Learning benchmarks.
References in corpus (26)
- The Seventh Data Release of the Sloan Digital Sky Survey
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems
- The Dependence of Star Formation History and Internal Structure on Stellar Mass for 10^5 Low-Redshift Galaxies
- The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories
- A New Non-Parametric Approach to Galaxy Morphological Classification
- Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
- The Asymmetry of Galaxies: Physical Morphology for Nearby and High Redshift Galaxies
- Physical properties and environments of nearby galaxies
- zCOSMOS - 10k-bright spectroscopic sample. The bimodality in the Galaxy Stellar Mass Function: exploring its evolution with redshift
- A New Approach to Galaxy Morphology: I. Analysis of the Sloan Digital Sky Survey Early Data Release
- Improving galaxy morphologies for SDSS with Deep Learning
- Structural and Photometric Classification of Galaxies - I. Calibration Based on a Nearby Galaxy Sample
- A Correlation between Galaxy Light Concentration and Supermassive Black Hole Mass
- Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
- On the Fraction of Barred Spiral Galaxies
- CLASH: Precise New Constraints on the Mass Profile of Abell 2261
- Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study
- Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
- Morpheus Reveals Distant Disk Galaxy Morphologies with JWST: The First AI/ML Analysis of JWST Images
- Is the Gini coefficient a stable measure of galaxy structure?
- Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
- Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
- Gradient Pattern Analysis Applied to Galaxy Morphology
- Complex Entropic Forms for Gradient Pattern Analysis of Spatio-temporal Dynamics
- Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks