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.
|Commitee:||Apai, Daniel, Barman, Travis, Koskinen, Tommi, Swain, Mark|
|School:||The University of Arizona|
|School Location:||United States -- Arizona|
|Source:||DAI-B 82/3(E), Dissertation Abstracts International|
|Keywords:||Exoplanet, Machine learning, Observational astronomy, Occultation, Radiative transfer|
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