[Rapid identification of variable star spectrum based on information entropy].

Guang Pu Xue Yu Guang Pu Fen Xi

School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China.

Published: January 2012

AI Article Synopsis

  • Variable stars play a crucial role in understanding the origins and evolution of the universe, but identifying them from complex spectrum data poses significant challenges.
  • Traditional methods for detecting variable stars often struggle with high time complexity and yield unclear results, making them less efficient.
  • The proposed approach utilizes information entropy to streamline the identification process, significantly reducing time complexity and improving the clarity of results, with initial tests showing promising effectiveness using Sloan star spectrum data.

Article Abstract

Variable star is very important for mankind studying cosmic origin and evolution. For studying variable star, the chief difficulty results from the filtration and identification of variable star, that is how to validly identify variable star spectra from large high-dimensional star spectra data. The traditional outlier definition tries to find the difference between the outlier data and the general model by different ways, and then the result is quantitatively analyzed and filtrated. However, the time complexity of this method is over size and its results are inscrutable and unaccountable. Information entropy is a measure of the uncertainty associated with a random variable. In the present paper, information entropy is imported as the standard of dataset common mode. A novel method is proposed to identify the variable star spectrum rapidly based on information entropy. The time complexity of this method is observably reduced and the man-made impact is effectively overcome. The preliminary experimental results based on Sloan star spectrum data show that the method is workable for rapid identification of variable star spectrum.

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