Identification and Classification of Exoplanets Using Machine Learning
  Techniques
Prithivraj G Alka Kumari
NASA's Kepler Space Telescope has been instrumental in the task of finding
the presence of exoplanets in our galaxy. This search has been supported by
computational data analysis to identify exoplanets from the signals received by
the Kepler telescope. In this paper, we consider building upon some existing
work on exoplanet identification using residual networks for the data of the
Kepler space telescope and its extended mission K2. This paper aims to explore
how deep learning algorithms can help in classifying the presence of exoplanets
with less amount of data in one case and a more extensive variety of data in
another. In addition to the standard CNN-based method, we propose a Siamese
architecture that is particularly useful in addressing classification in a
low-data scenario. The CNN and ResNet algorithms achieved an average accuracy
of 68% for three classes and 86% for two-class classification. However, for
both the three and two classes, the Siamese algorithm achieved 99% accuracy.