Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms

HIGHLIGHTS

• who: Colazo M. and colleagues from the Instituto de Astronomu00eda Teu00f3rica y Experimental, CONICET-UNC, Laprida, Cu00f3rdoba, Argentina Instituto de Fu00edsica Aplicada las Ciencias y las Tecnologu00edas, Universidad de Alicante, San Vicent del Raspeig, Alicante have published the paper: Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms, in the Journal: (JOURNAL)
• what: The authors aim to produce new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. The motivation of this type of research is to explain the initial composition of . . .
  

   

  

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