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    The Detection and Characterization of Transiting Exoplanets

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    Author
    Pearson, Kyle
    Issue Date
    2020
    Keywords
    Exoplanet
    Machine learning
    Observational Astronomy
    Occultation
    Radiative Transfer
    Advisor
    Griffith, Caitlin
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    The existence of worlds beyond our own has been a subject of fascination and inspiration since the times of the ancient Greeks. The first exoplanet discovery in Wolszczan and Frail 1992 led to a revolution that sparked the scientific community to develop new space missions (e.g. Kepler, TESS and ARIEL) and instruments (e.g. HARPS, GPI, etc.) purely dedicated to exoplanet science (Borucki et al. 2010; Ricker et al. 2015; Tinetti et al. 2016; Mayor et al. 2003; Macintosh et al. 2006). The thousands of exoplanets discovered over the past decade have mostly been Earth-sized planets around low-mass stars. The potential of habitable planets drives the field towards detailed spectroscopic observations to better characterize their mass and/or atmospheric composition. Planetary search surveys from the ground and space are expected to detect more exoplanets orbiting nearby stars, which is conducive for atmospheric characterization. This dissertation addresses two main questions, how can we identify which stars have transiting exoplanets and what are the atmospheres of these planets made of? Currently, the transit method of detection is one of the most successful tools for probing the size and orbits of planetary systems. However, for Earth-sized planets the signal is small (∼100 ppm for a Sun-like star) and comparable to the photometric noise from the host star (∼0.1-1%). The manual interpretation of such data is labor-intensive and subject to human error, the results of which can be difficult to quantify. I present a new method for combining existing techniques with machine learning in order to expedite, automate, and increase the robustness of processing large observational data sets. The technique is applied to Kepler and TESS data where I find evidence for 3 new multi-planet systems. The second part of my dissertation focuses on atmospheric characterization where I use spectroscopic observations to search for signatures of Na in the hot-Jupiter XO-2b.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Planetary Sciences
    Degree Grantor
    University of Arizona
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