In data from telescopes, exoplanet signals are masked by complex instrumental and astrophysical noise sources of apriori unknown form. Conventional exoplanet-detection pipelines perform sequential data processing to mitigate noise, relying on incomplete signal models at each step, which can lower overall detection performance. My research analyzes the statistical properties of exoplanet data-processing architectures to design robust data-driven signal models and tractable joint-modeling approaches that improve the detection of exoplanet signals, bringing us closer to finding faint Earth-like planets submerged by the brightness of Sun-like stars.
- Science Mentor: Lia Corrales, Astronomy, LSA
- AI Mentor: Alfred Hero, Electrical Engineering and Computer Science, College of Engineering
- Research Theme: Exoplanets