Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
arXiv:2507.14072 · doi:10.1051/0004-6361/202555620
Abstract
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution; however, the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL framework combining K-means clustering with random forest classification. Our method partitions unlabeled data into 50 clusters, propagates labels from spectroscopically confirmed centroids to 95% of cluster members, and trains a random forest on the expanded pseudo-labeled dataset. We applied this to the CPz catalog, containing multi-survey photometric and spectroscopic data, and compared performance with a fully supervised random forest. Our SSL approach achieves F1 scores of 98.8%, 98.9%, and 92.0% for stars, galaxies, and quasars, respectively, closely matching the supervised method with F1 scores of 99.1%, 99.1%, and 93.1%, while outperforming traditional color-cut techniques. The method demonstrates robustness in high-dimensional feature spaces and superior label efficiency compared to prior work. This work highlights SSL as a scalable solution for astronomical classification when labeled data is limited, though performance may be degraded in lower dimensional settings.
9 pages, 9 figures, 2 tables, Accepted for Publication in A&A
References in corpus (11)
- A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
- Galaxy And Mass Assembly (GAMA): Assimilation of KiDS into the GAMA database
- Unsupervised star, galaxy, qso classification: Application of HDBSCAN
- Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue
- A review of unsupervised learning in astronomy
- Photometric redshift-aided classification using ensemble learning
- Photometric identification of compact galaxies, stars and quasars using multiple neural networks
- Wide Area VISTA Extra-galactic Survey (WAVES): Unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and
- Semi-Supervised Classification and Clustering Analysis for Variable Stars
- J-PLUS DR3: Galaxy-Star-Quasar classification
- Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data