/Exoplanet-Hunting

Machine Learning Project: Exoplanet Hunting in Deep Space

Primary LanguageJupyter Notebook

ML Project: Exoplanet Hunting in Deep Space

Alken Rrokaj, Fatjon Barçi

Motivation:

Exoplanet hunting in deep space is done by tracking a star over several months or years, to observe if there is a regular 'dimming' of the flux (the light intensity). This is light dimming, is evidence that there may be an orbiting body around the star, such as a planet. This star could be considered to be a 'candidate' system for further depth observations, for example by a satellite that captures light at a different wavelength, could solidify the belief that the candidate can in fact be 'confirmed'. Using a machine learning model is probably the only logical method of making this tedious task possible.

Dataset Description:

Exoplanet Hunting in Deep Space

  • Column 1 is the label vector.
  • Columns 2 - 3198 are the flux values over time.

Trainset:

  • 5087 rows or observations.
  • 3198 columns or features.
  • 37 confirmed exoplanet-stars and 5050 non-exoplanet-stars.

Testset:

  • 570 rows or observations.
  • 3198 columns or features.
  • 5 confirmed exoplanet-stars and 565 non-exoplanet-stars.

Poster

Poster

Paper is available here