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Abstract

Machine learning for the classification and feature prediction of the morphologies of stars, galaxies, quasars, and galaxies

Author(s): Angela Guerrero

 It is highly tiresome to identify astronomical objects manually now, which was the usual method for a very long period in an era with constantly replenishing data. Galaxies are collections of stars that dot the cosmos. We can learn a great deal about our own galaxy through the study and analysis of these other galaxies. For the classification and feature prediction of galaxy morphology, a deep convolutional neural network architecture is suggested. Based on these properties, the galaxy may be classified into eleven morphological classes. Two models—one for class prediction and the other for galaxy feature prediction—make up the suggested architecture. Using information from the SDSS and LAMOST Surveys, as well as spectroscopic data analysis, they categories celestial objects into Galaxies, Stars, and Quasars. For the classification job, baseline techniques are compared, and enhanced, optimised classification is further investigated. 


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Citations : 214

Journal of Space Exploration received 214 citations as per Google Scholar report

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  • Open J Gate
  • China National Knowledge Infrastructure (CNKI)
  • Cosmos IF
  • Directory of Research Journal Indexing (DRJI)
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