Date of Award

Spring 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Committee Director

Yaohang Li

Committee Member

Lusi Li

Committee Member

Sampath Jayarathna

Abstract

The search for exoplanets has been an ongoing effort since the first discoveries of planets beyond our solar system in the 1990s. Finding a potentially habitable planet outside our solar system could provide key insights on life elsewhere in the universe. NASA Missions such as the Kepler, launched in 2009 and completed in 2018, have provided a massive amount of data in this goal by using the transit method to discover repetitive and periodic dips in visible light around a star. The transit method has been used to measure flux, the brightness of a star over time. These flux time series are referred to as light curves, some of which may belong to that of an exoplanet. Machine learning is a powerful tool that can help identify complex patterns in large datasets and increase the efficiency of the exoplanet vetting process. Utilization of machine learning on confirming light curves that belong to exoplanets has already been conducted in different manners on several NASA Mission datasets, including Kepler. Projects such as Robovetter and Autovetter attempted to use classical methods (decision tree and random forest modeling, respectively) to recreate the manual decision process of classifying exoplanets. Apart from classical methods, deep learning has been used to automatically vet the light curve data. After phase folding the light curve so that the transit event is in the center, a Global View is created with a set amount of equally sized bins. In addition to the Global View, the Local View is created in a similar manner, except a Local View zooms into the transit event. The Global Views and Local Views are then passed into a one dimensional convolutional neural network (1D CNN). Newer solutions include a Transformer-based classification model applied to the TESS light curve dataset to distinguish planetary transits from false positives with success.

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DOI

10.25777/24td-jc94

ISBN

9798382770727

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