Identifying Potentially Habitable Exoplanets: A Study using the Transit Method and Kepler dataset

Abstract

An exoplanet is a planet that orbits a star outside of our solar system. The study of exoplanets is an active area of research in astronomy. In this research, we aim to utilize the Kepler dataset provided by NASA EXOPLANET ACRCHIEVE to identify and classify exoplanets that could potentially support life. The Kepler dataset, which comprises of observations of over 150,000 stars, has been instrumental in the discovery of thousands of exoplanets. We will analyse the dataset using machine learning techniques to classify exoplanets as potentially habitable based on their orbital period, size, distance from their host star, and other parameters. The findings of this research will greatly enhance our understanding of the frequency of life in the universe and the use of machine learning techniques on the Kepler dataset will be an essential tool in the quest for finding potentially habitable exoplanets. Emerging Machine Learning Algorithms aid in detecting habitability of exoplanet in different stellar system. For finding an Exoplanet we have used the transit method which is based on the principle that when an exoplanet passes in front of its host star, it causes a temporary dip in the star's brightness. By monitoring the brightness of a star over time, scientists can detect these periodic dips and use them to infer the presence of an exoplanet. The findings of this research have the potential to significantly advance our understanding of the prevalence of life in the universe.