Classification of Variable Star Light Curves with Convolutional Neural Network
The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and s...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Galaxies |
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| Online Access: | https://www.mdpi.com/2075-4434/12/6/75 |
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| author | Almat Akhmetali Timur Namazbayev Gulnur Subebekova Marat Zaidyn Aigerim Akniyazova Yeskendyr Ashimov Nurzhan Ussipov |
| author_facet | Almat Akhmetali Timur Namazbayev Gulnur Subebekova Marat Zaidyn Aigerim Akniyazova Yeskendyr Ashimov Nurzhan Ussipov |
| author_sort | Almat Akhmetali |
| collection | DOAJ |
| description | The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys. |
| format | Article |
| id | doaj-art-fd94c6eeaed4469d9307b79cc9443603 |
| institution | Kabale University |
| issn | 2075-4434 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Galaxies |
| spelling | doaj-art-fd94c6eeaed4469d9307b79cc94436032024-12-27T14:27:34ZengMDPI AGGalaxies2075-44342024-11-011267510.3390/galaxies12060075Classification of Variable Star Light Curves with Convolutional Neural NetworkAlmat Akhmetali0Timur Namazbayev1Gulnur Subebekova2Marat Zaidyn3Aigerim Akniyazova4Yeskendyr Ashimov5Nurzhan Ussipov6Department of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanDepartment of Electronics and Astrophysics, Al-Farabi Kazakh National University, 050040 Almaty, KazakhstanThe classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.https://www.mdpi.com/2075-4434/12/6/75variable starlight curvemachine learningconvolutional neural networkrandom forest |
| spellingShingle | Almat Akhmetali Timur Namazbayev Gulnur Subebekova Marat Zaidyn Aigerim Akniyazova Yeskendyr Ashimov Nurzhan Ussipov Classification of Variable Star Light Curves with Convolutional Neural Network Galaxies variable star light curve machine learning convolutional neural network random forest |
| title | Classification of Variable Star Light Curves with Convolutional Neural Network |
| title_full | Classification of Variable Star Light Curves with Convolutional Neural Network |
| title_fullStr | Classification of Variable Star Light Curves with Convolutional Neural Network |
| title_full_unstemmed | Classification of Variable Star Light Curves with Convolutional Neural Network |
| title_short | Classification of Variable Star Light Curves with Convolutional Neural Network |
| title_sort | classification of variable star light curves with convolutional neural network |
| topic | variable star light curve machine learning convolutional neural network random forest |
| url | https://www.mdpi.com/2075-4434/12/6/75 |
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