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Machine learning techniques for classifying dangerous asteroids.

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This study classified asteroid hazards using machine learning, comparing Extra Tree, Random Forest, and Gradient Boosting models. The findings aid in identifying and mitigating risks from near-Earth objects.

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Area of Science:

  • Astronomy and Astrophysics
  • Planetary Science
  • Computer Science (Machine Learning)

Background:

  • Outer space contains numerous celestial objects, including asteroids, some of which pose potential hazards to Earth.
  • Understanding and classifying these near-Earth objects is crucial for planetary defense and risk assessment.

Purpose of the Study:

  • To classify the hazards posed by asteroids in Earth's vicinity.
  • To compare the performance of various machine learning algorithms in identifying high-risk asteroids.

Main Methods:

  • Reviewed NASA-certified asteroids classified as near-Earth objects.
  • Employed hyperparameter tuning for Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost algorithms.
  • Investigated asteroid risks using these machine learning models.

Main Results:

  • The study successfully classified asteroid hazards using multiple machine learning algorithms.
  • Receiver Operating Characteristic (ROC) curves were generated and compared to evaluate algorithm performance.
  • Identified the most effective algorithms for classifying high-risk asteroids.

Conclusions:

  • Machine learning models, particularly those tuned with hyperparameter optimization, are effective tools for assessing asteroid risks.
  • Comparative analysis of algorithms like Extra Tree, Random Forest, and Gradient Boosting provides insights into their classification capabilities.
  • This research contributes to improved methods for identifying and potentially mitigating threats from near-Earth asteroids.