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[A fractal denoising method for astronomical spectral signal].

Guang pu xue yu guang pu fen xi = Guang pu·2012
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[Automatic recognition of M-star spectral subtype based on fractal coding].

Jin-Shu Han1

  • 1Department of Computer Science and Technology, Dezhou University, Dezhou 253020, China. jinshu_han@yahoo.com.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|October 29, 2013
PubMed
Summary

This study introduces fractal coding to classify astronomical spectra, demonstrating its effectiveness in identifying M star subtypes from LAMOST and SDSS data, even with noise and calibration errors.

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Area of Science:

  • Astronomy and Astrophysics
  • Data Science
  • Signal Processing

Context:

  • Astronomical spectra contain rich information for stellar classification.
  • Existing methods may be sensitive to noise and calibration errors.
  • Developing robust spectral analysis techniques is crucial.

Purpose:

  • To apply fractal coding for astronomical spectral subtype recognition for the first time.
  • To evaluate the method's performance, including noise immunity and resistance to calibration errors.
  • To test the method on M star subtypes from LAMOST and SDSS datasets.

Summary:

  • Astronomical spectral data were coded into three bands (400-510, 600-700, 780-900 nm) based on local fractal features.
  • A fractal coding method, utilizing matching data block position and minimum matching error, was employed for subtype recognition.
  • The fractal coding method demonstrated noise immunity and resilience to calibration errors and effective curves of LAMOST, successfully recognizing M star subtypes.

Impact:

  • Provides a novel and robust method for astronomical spectral classification.
  • Enhances the accuracy and reliability of identifying stellar subtypes, particularly M stars.
  • Offers a potential tool for analyzing large astronomical datasets like those from LAMOST and SDSS.