Globular Cluster Classification in Galaxy M81 Using Machine Learning Techniques
Autor: | Benjamas Panyangam, Chutipong Suwannajak, Prapaporn Techa-Angkoon, Phatcharapon Sookmee, Nahathai Tanakul |
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Rok vydání: | 2020 |
Předmět: |
0303 health sciences
Artificial neural network Computer science business.industry Feature extraction Decision tree Machine learning computer.software_genre 01 natural sciences Galaxy Random forest Support vector machine 03 medical and health sciences Globular cluster 0103 physical sciences Galaxy formation and evolution Artificial intelligence business 010303 astronomy & astrophysics computer Astrophysics::Galaxy Astrophysics 030304 developmental biology |
Zdroj: | JCSSE |
DOI: | 10.1109/jcsse49651.2020.9268348 |
Popis: | Globular clusters are very important in astronomy since they can be used to study the process of galaxy formation and evolution. With the exponential growth of data in astronomy, it is currently inefficient to classify globular clusters from the other types of astronomical objects by humans. In this study, we explored the possibility of using machine learning in globular cluster classification to replace the classification by humans. We selected five standard classification methods including k-NN, Random Forest, SVM, Neural Network, and Decision Tree. All models were built and tested by using the Weka software with datasets from a galaxy M81. Our experiments showed that k-NN, Random Forest, and SVM are the best approaches for globular cluster classification, with 97.7% accuracy, 97.8% precision, 97.7% recall, and 97.7% F-measure. Finally, when we applied these models to an unseen dataset to predict new globular cluster candidates, we acquired 6.32% success rate compared to 30% success rate by humans. This suggests that machine learning techniques can be applied to globular clusters classification. However, our models need to be improved to achieve a higher success rate to replace the classification by humans. |
Databáze: | OpenAIRE |
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