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Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
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Deep learning exoplanets detection by combining real and synthetic data.

Sara Cuéllar1, Paulo Granados1, Ernesto Fabregas2

  • 1Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.

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This study developed a deep learning system to detect exoplanet transits using Kepler Telescope data. Training with synthetic data significantly improved the system's performance on real exoplanet light curves.

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Area of Science:

  • Astronomy and Astrophysics
  • Machine Learning

Background:

  • Exoplanet discovery is crucial, with over 4300 confirmed planets.
  • Planetary transit detection is a key method for finding exoplanets.
  • Kepler Telescope light curves provide valuable data for exoplanet research.

Purpose of the Study:

  • To develop a deep learning system for detecting planetary transits.
  • To enhance transit detection using real Kepler Telescope light curves.
  • To optimize the use of synthetic data in training deep learning models.

Main Methods:

  • A Convolutional Neural Network (CNN) classification model was developed.
  • The model was trained on a mix of real and synthetic Kepler light curve data.
  • Model performance was validated using unknown, real light curve data.

Main Results:

  • The optimal ratio of synthetic data was determined through optimization and sensitivity analysis.
  • The trained model demonstrated improved precision, accuracy, and true positive rates.
  • Performance was compared favorably against other similar studies.

Conclusions:

  • Synthetic data can effectively enhance the training of deep learning models for exoplanet transit detection.
  • The developed system shows promise for improving the efficiency and accuracy of exoplanet discovery.
  • This approach contributes to advancing the field of exoplanetary science.