Exoplanet Detection using Machine Learning

We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of these methods to improve the conventional algorithm based approach used in astrophysics today to detect exoplanets. We used the popular time-series analysis library 'TSFresh' to extract features from lightcurves. For each lightcurve, we extracted 789 features. These features capture information about the characteristics of a lightcurve. We used these features later to train a tree-based classifier using a popular machine learning tool 'lightgbm'. This was tested on simulated data which proved it to be more effective than conventional box least squares fitting (BLS). It produced comparable results to the existing state-of-art models while being much more computationally efficient and without needing folded and secondary views of the lightcurves. On Kepler data, the method is able to predict a planet with an AUC of 0.948 which means that, 94.8% of the time a planet signal is ranked higher than a non-planet signal and Recall of 0.96 meaning, 96% of real planets are classified as planets. With the Nasa's Transiting Exoplanet Survey Satellite (TESS), a reliable classification system is much needed as we are receiving over a million lightcurves per month. However, classification is harder as lightcurves are shorter. Our method is able to classify lightcurves with an accuracy of 98% and is able to identify planets with a Recall of 0.82.