Exoplanet Detection : A Detailed Analysis

TL;DR

This paper provides a comprehensive analysis of exoplanet detection methods, comparing traditional techniques and machine learning approaches, and discusses recent discoveries and their implications for understanding planetary systems.

Contribution

It offers an in-depth comparison of detection methodologies and explores the application of machine learning algorithms in exoplanet detection, highlighting recent significant discoveries.

Findings

Comparison of detection methods and their effectiveness

Application of machine learning algorithms in detection

Summary of recent exoplanet discoveries

Abstract

The exoplanet detection is the most exciting and challenging field of astronomy. The discovery of many exoplanets has revolutionized our understanding of the formation and evolution of planetary systems and has showed new ways to search for extra terrestrial life. In recent years, some primary methods of exoplanet detection like transit, radial velocity, gravitational microlensing, direct imaging and astrometry have played a important role for the discovery of exoplanets. In this paper we explored detection methodologies with all the implications and analytics of comparison between them. Here we also discussed on different machine learning algorithms for exoplanet detection and visualization. Finally, concluded with the significant discoveries made by some missions and their implications on our understanding for the properties, environmental conditions and importance of exoplanets in the universe.