Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy
Astronomy is experiencing rapid growth in the size and complexity of data. This reinforces the development of data-driven science as a useful complement to the current model of model-based data analysis. In spite of this, traditional merger analysis of catalogs is mostly done through visual inspecti...
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| Format: | Article |
| Language: | English |
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Damghan university
2022-04-01
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| Series: | Iranian Journal of Astronomy and Astrophysics |
| Subjects: | |
| Online Access: | https://ijaa.du.ac.ir/article_298_81a2e5b0f87c6a38534099fa65d19bfb.pdf |
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| author | Samira Monfared Neda Abdolvand Mohammad Taghi Mirtorabi Saeedeh Rajaee Harandi |
| author_facet | Samira Monfared Neda Abdolvand Mohammad Taghi Mirtorabi Saeedeh Rajaee Harandi |
| author_sort | Samira Monfared |
| collection | DOAJ |
| description | Astronomy is experiencing rapid growth in the size and complexity of data. This reinforces the development of data-driven science as a useful complement to the current model of model-based data analysis. In spite of this, traditional merger analysis of catalogs is mostly done through visual inspection by trained experts. A method that is not efficient today, because the subjectivity of manual classification has made the result of the process very dependent on the intuition of the analyst and the type and quality of the image. Hence, this study focuses on data processing methods based on Artificial Intelligence (AI) algorithms and investigates the possibility of a pattern among astronomical data to predict the merger of galaxies. The modeling is done in two phases. The first phase deals with the classification between minority and majority classes and the second phase search for any distinction between minority classes. In both phases, various algorithms such as Naive Bayes, Random Forest, and Generalized linear model (GLM) and Neural network are used to ensure the best results according to the research data. The best results for both phases obtained from the implementation of the GLM algorithm with the accuracy of 70.28 % and 76.51% for the first and second phase respectively. |
| format | Article |
| id | doaj-art-edd500ec4a9b4fd9a59d35cf8ccaab03 |
| institution | Kabale University |
| issn | 2322-4924 2383-403X |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Damghan university |
| record_format | Article |
| series | Iranian Journal of Astronomy and Astrophysics |
| spelling | doaj-art-edd500ec4a9b4fd9a59d35cf8ccaab032025-01-12T10:09:59ZengDamghan universityIranian Journal of Astronomy and Astrophysics2322-49242383-403X2022-04-0191193010.22128/ijaa.2022.554.1122298Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared SpectroscopySamira Monfared0Neda Abdolvand1Mohammad Taghi Mirtorabi2Saeedeh Rajaee Harandi3Department of Management, Faculty of social Sciences and Economics, Alzahra University.Department of Management, Faculty of Social Sciences and Economics, Alzahra UniversityDepartment of Physics, Alzahra University, Tehran, P.O.Box 1993891176, IranDepartment of Management, Faculty of Social Sciences and Economics, Alzahra UniverdsityAstronomy is experiencing rapid growth in the size and complexity of data. This reinforces the development of data-driven science as a useful complement to the current model of model-based data analysis. In spite of this, traditional merger analysis of catalogs is mostly done through visual inspection by trained experts. A method that is not efficient today, because the subjectivity of manual classification has made the result of the process very dependent on the intuition of the analyst and the type and quality of the image. Hence, this study focuses on data processing methods based on Artificial Intelligence (AI) algorithms and investigates the possibility of a pattern among astronomical data to predict the merger of galaxies. The modeling is done in two phases. The first phase deals with the classification between minority and majority classes and the second phase search for any distinction between minority classes. In both phases, various algorithms such as Naive Bayes, Random Forest, and Generalized linear model (GLM) and Neural network are used to ensure the best results according to the research data. The best results for both phases obtained from the implementation of the GLM algorithm with the accuracy of 70.28 % and 76.51% for the first and second phase respectively.https://ijaa.du.ac.ir/article_298_81a2e5b0f87c6a38534099fa65d19bfb.pdfgalaxy morphologygalaxy mergenear-infrared spectroscopygalaxy zoomachine learning |
| spellingShingle | Samira Monfared Neda Abdolvand Mohammad Taghi Mirtorabi Saeedeh Rajaee Harandi Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy Iranian Journal of Astronomy and Astrophysics galaxy morphology galaxy merge near-infrared spectroscopy galaxy zoo machine learning |
| title | Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy |
| title_full | Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy |
| title_fullStr | Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy |
| title_full_unstemmed | Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy |
| title_short | Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy |
| title_sort | machine learning method for predicting the merge and morphology of galaxies through near infrared spectroscopy |
| topic | galaxy morphology galaxy merge near-infrared spectroscopy galaxy zoo machine learning |
| url | https://ijaa.du.ac.ir/article_298_81a2e5b0f87c6a38534099fa65d19bfb.pdf |
| work_keys_str_mv | AT samiramonfared machinelearningmethodforpredictingthemergeandmorphologyofgalaxiesthroughnearinfraredspectroscopy AT nedaabdolvand machinelearningmethodforpredictingthemergeandmorphologyofgalaxiesthroughnearinfraredspectroscopy AT mohammadtaghimirtorabi machinelearningmethodforpredictingthemergeandmorphologyofgalaxiesthroughnearinfraredspectroscopy AT saeedehrajaeeharandi machinelearningmethodforpredictingthemergeandmorphologyofgalaxiesthroughnearinfraredspectroscopy |