Classification of Stars using Stellar Spectra collected by the Sloan Digital Sky Survey
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
7-14-2019
Abstract
The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). We use the SDSS dataset since it is the most important stellar spectra database available today. In our approach, we apply redshift corrections to the spectra and reduce the number of flux measurements by feature selection. Then we apply standard classifier methods: Random Forest and Support Vector Machine. We compare the accuracy of feature selection and classifier combinations for redshifted stellar spectra and rest stellar spectra. Even though redshifted stellar spectra create feature matrix discrepancies, classifiers utilizing redshifted stellar spectra perform with high accuracy. This creates a viable option for automated classification of stellar spectra without having to identify the redshift value.
Recommended Citation
Brice, M., & Andonie, R. (2019). Classification of Stars using Stellar Spectra collected by the Sloan Digital Sky Survey. 2019 International Joint Conference on Neural Networks (IJCNN), 19028301. https://doi.org/10.1109/ijcnn.2019.8852407
Journal
2019 International Joint Conference on Neural Networks (IJCNN)
Rights
Copyright © 2019, IEEE
Comments
This article was originally published in 2019 International Joint Conference on Neural Networks (IJCNN). The article from the publisher can be found here.
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