Temperature- and Color-Based SDSS Stellar Spectral Classification Using Automated Scheme

  • Amit Goyal
  • Jayash Kumar Sharma
  • Darpan Anand
  • Manish Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)

Abstract

Automated techniques minimize the complexity, saving time and efforts in the object classification and their analysis. Sloan Digital Sky Survey (SDSS) is one of the spectroscopic surveys releasing large data sets. Astronomers are looking for some automated techniques so that they can analyze these massive data sets which are now publicly available. We use Feed Forward Back Propagation (FFBP) Neural Network for automatic classification. Classification of stars is performed on the basis of two parameters that are temperature and color. 1500 SDSS spectra are classified into 4 spectral types, and around 2359 SDSS spectra are classified into 7 spectral types ranging from A to K and O to M type stars by using color and temperature, respectively.

Keywords

Stellar spectra Spectral type Sloan digital sky survey (SDSS) Neural network 

References

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Amit Goyal
    • 1
  • Jayash Kumar Sharma
    • 1
  • Darpan Anand
    • 1
  • Manish Gupta
    • 2
  1. 1.Hindustan Institute of Technology and ManagementAgraIndia
  2. 2.Amity UniversityGwaliorIndia

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