  We are at a unique timeline in the history of human evolution where we may be
able to discover earth-like planets around stars outside our solar system where
conditions can support life or even find evidence of life on those planets.
With the launch of several satellites in recent years by NASA, ESA, and other
major space agencies, an ample amount of datasets are at our disposal which can
be utilized to train machine learning models that can automate the arduous
tasks of exoplanet detection, its identification, and habitability
determination. Automating these tasks can save a considerable amount of time
and minimize human errors due to manual intervention. To achieve this aim, we
first analyze the light intensity curves from stars captured by the Kepler
telescope to detect the potential curves that exhibit the characteristics of an
existence of a possible planetary system. For this detection, along with
training conventional models, we propose a stacked GBDT model that can be
trained on multiple representations of the light signals simultaneously.
Subsequently, we address the automation of exoplanet identification and
habitability determination by leveraging several state-of-art machine learning
and ensemble approaches. The identification of exoplanets aims to distinguish
false positive instances from the actual instances of exoplanets whereas the
habitability assessment groups the exoplanet instances into different clusters
based on their habitable characteristics. Additionally, we propose a new metric
called Adequate Thermal Adequacy (ATA) score to establish a potential linear
relationship between habitable and non-habitable instances. Experimental
results suggest that the proposed stacked GBDT model outperformed the
conventional models in detecting transiting exoplanets. Furthermore, the
incorporation of ATA scores in habitability classification enhanced the
performance of models.
