Modeling and Removal of Scattered Light in TESS Full Frame Images Using Generative AI
Year: 2024
Type: article
Abstract: Scattered light from the Earth and Moon can significantly impact the background levels in TESS (Transiting Exoplanet Survey Satellite) full frame images (FFIs), hindering the search for transiting exoplanets and other astronomical phenomena. We present a novel approach to model and remove scattered light at the image level using deep learning. We have developed a conditional diffusion model that accurately captures the scattered light patterns in FFIs probabilistically, using only the angles and distances of the Earth and Moon with respect to the TESS cameras. The model learns the complex, dynamic patterns of scattered light and produces corrected FFIs along with uncertainties. This deep learning approach opens up new avenues for scattered light and systematics correction in present and future space based surveys.
Authors: Lupo, Joseph, Baek, Jenny, Muthukrishna, Daniel, Vanderspek, Roland
Language: English
Subfield: Instrumentation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6892973251