Exoplanet Detection by Transit Method

Hati, Arnab (2023) Exoplanet Detection by Transit Method. Masters thesis, Dublin, National College of Ireland.

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Abstract

The detection of exoplanets is essential for understanding the diversity of the universe, habitability opportunities, and the possibility of alien life outside our solar system. Extraterrestrial life has been the focus of extensive research for decades. To detect exoplanets, many machine learning and deep learning approaches have provided important predictions. The transit method or observation is responsible for the variations in a star’s spectrum caused by an orbiting planet’s gravitational pull. To improve these predictions, this research concentrates on the implementation of machine learning and the deep learning model after the feature selection technique is applied. Two machine learning models (XGboost, Catboost) and three deep learning models (RNN, Variableational Encoder, GRU) were implemented. Once the best feature was selected to improve the overall performance, Catboost outperformed the other machine learning and deep learning models by 99.98%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QB Astronomy Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 May 2025 15:57
Last Modified:	08 May 2025 15:57
URI:	https://norma.ncirl.ie/id/eprint/7525

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