Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/41924| Title: | Machine learning for galaxy morphology classification |
| Authors: | Gauci, Adam Zarb Adami, Kristian Abela, John |
| Keywords: | Machine learning -- Statistical methods Galaxies -- Classification |
| Issue Date: | 2010 |
| Publisher: | Oxford University Press |
| Citation: | Gauci, A., Zarb Adami, K., & Abela, J. (2010). Machine learning for galaxy morphology classification. Monthly Notices of the Royal Astronomical Society, 1-8. |
| Abstract: | In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue. |
| URI: | https://www.um.edu.mt/library/oar//handle/123456789/41924 |
| ISSN: | 00358711 |
| Appears in Collections: | Scholarly Works - FacSciPhy |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Machine_learning_for_galaxy_morphology_classification.pdf | 7.11 MB | Adobe PDF | View/Open |
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