Predicting Habitable Exoplanets in Different Star-Systems Using Deep Learning Based Anomaly Detection Approach

Mankind has been looking up at the stars for centuries, wondering what lies in deep space, and if other civilizations like ours exist. Following this, with the significant advancement in the field of cosmology and space missions, there has been an exponential increase in the astronomical data collected by space telescopes to explore the possibility of harboring extraterrestrial life. And there is still no consensus on whether a planet is habitable, potentially habitable or inhabitable. The only habitable planet is Earth, therefore, the hypothesized exoplanets are categorized using Earth as a reference, also known as the “Earth Habitability Index” (EHI). In this regard, a number of additional metrics have been developed to categorize an exoplanet's habitability score, such as the Cobb-Douglas Habitability Score, which is a metric based on the Cobb-Douglas habitability production function (CD-HPF). Many classification-based algorithms have already been developed, but they have limitations, such as the possibility of misleading accuracy scores when applied to highly unbalanced datasets. Recently, some work has also been proposed in anomaly detection using memetic algorithms, which belong to the class of metaheuristic algorithms, but the number of feature sets used is significantly less compared to the ones impacting the habitability score of an exoplanet. In this present research, we are proposing a novel variational auto-encoder algorithm that works on a probability distribution function belonging to the class of anomaly detection and will work on a greater number of features in a significantly larger dataset. The proposed algorithm follows an unsupervised learning approach to detect anomalies in order to determine potentially habitable exoplanets. To validate the approach, the obtained results will be cross-matched with the dataset provided by the Planetary Habitability Laboratory's habitable exoplanet catalog (PHL-HEC).