Assessing Exoplanet Habitability through Data-driven Approaches: A Comprehensive Literature Review

TL;DR

This comprehensive literature review examines how machine learning techniques are advancing exoplanet detection, classification, and visualization, highlighting recent trends, datasets, and the evolving role of computational models in astronomical research.

Contribution

It provides a detailed synthesis of fifteen key papers, showcasing the integration of machine learning methods like SVM and Deep Learning in exoplanet research, and discusses future directions.

Findings

Increased use of datasets like Kepler and TESS.

Improved accuracy in exoplanet detection models.

Growing reliance on machine learning techniques.

Abstract

The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This literature the review aims to illuminate the emerging trends and advancements within this sphere, specifically focusing on the interplay between exoplanet detection, classification, and visualization, and the the increasingly pivotal role of machine learning and computational models. Our journey through this realm of exploration commences with a comprehensive analysis of fifteen meticulously selected, seminal papers in the field. These papers, each representing a distinct facet of exoplanet research, collectively offer a multi-dimensional perspective on the current state of the field. They provide valuable insights into the innovative application of machine learning techniques to overcome the challenges posed by the analysis and interpretation of astronomical data. From the application of Support Vector Machines (SVM) to Deep Learning models, the review encapsulates the broad spectrum of machine learning approaches employed in exoplanet research. The review also seeks to unravel the story woven by the data within these papers, detailing the triumphs and tribulations of the field. It highlights the increasing reliance on diverse datasets, such as Kepler and TESS, and the push for improved accuracy in exoplanet detection and classification models. The narrative concludes with key takeaways and insights, drawing together the threads of research to present a cohesive picture of the direction in which the field is moving. This literature review, therefore, serves not just as an academic exploration, but also as a narrative of scientific discovery and innovation in the quest to understand our cosmic neighborhood.