Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
10-17-2019
Abstract
The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature of stellar classification and radial velocity, these complications cannot be corrected. However, classifiers utilizing a large set of observed stellar spectra, which has had astronomical-specific feature selection applied, performed computationally fast with extremely high accuracy.
Recommended Citation
Brice, M. J., & Andonie, R. (2019). Automated Morgan Keenan Classification of Observed Stellar Spectra Collected by the Sloan Digital Sky Survey Using a Single Classifier. The Astronomical Journal, 158(5), 188. https://doi.org/10.3847/1538-3881/ab40d0
Journal
The Astronomical Journal
Rights
© 2019. The American Astronomical Society. All rights reserved.
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons, Stars, Interstellar Medium and the Galaxy Commons
Comments
This article was originally published in The Astronomical Journal. The full-text article from the publisher can be found here.