Automated Classification of Stellar Spectra using the Sloan Digital Sky Survey
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
16-5-2019
End Date
16-5-2019
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). The SDSS is used since it is the most important stellar spectra database available today. Stellar spectra from the SDSS are applied to standard classification methods such as K Nearest Neighbors, Random Forest, and Support Vector Machine. The dimensionality of the stellar spectra is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain specific astronomical knowledge. These methods are utilized to classify the stellar spectra into the two standard star classification schemes, the Harvard Spectral Classification and the Morgan Keenan Luminosity Classes. A physical phenomenon known as redshift causes machine learning complications when classifying stellar spectra. However, classifiers utilizing redshifted stellar spectra performed with high accuracy. This creates a viable option for automated classification of stellar spectra without having to correct for redshift.
Recommended Citation
Brice, Michael, "Automated Classification of Stellar Spectra using the Sloan Digital Sky Survey" (2019). Symposium Of University Research and Creative Expression (SOURCE). 175.
https://digitalcommons.cwu.edu/source/2019/Oralpres/175
Department/Program
Computer Science
Slides for SOURCE 2019 presentation Brice
Automated Classification of Stellar Spectra using the Sloan Digital Sky Survey
Ellensburg
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). The SDSS is used since it is the most important stellar spectra database available today. Stellar spectra from the SDSS are applied to standard classification methods such as K Nearest Neighbors, Random Forest, and Support Vector Machine. The dimensionality of the stellar spectra is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain specific astronomical knowledge. These methods are utilized to classify the stellar spectra into the two standard star classification schemes, the Harvard Spectral Classification and the Morgan Keenan Luminosity Classes. A physical phenomenon known as redshift causes machine learning complications when classifying stellar spectra. However, classifiers utilizing redshifted stellar spectra performed with high accuracy. This creates a viable option for automated classification of stellar spectra without having to correct for redshift.
https://digitalcommons.cwu.edu/source/2019/Oralpres/175
Faculty Mentor(s)
Razvan Andonie