Atmospheric Retrieval

Atmospheric retrieval aims to infer atmospheric properties—like temperature, pressure, and chemical composition—from observational data, such as light spectra from exoplanets or satellite imagery of Earth's surface. Current research heavily utilizes machine learning techniques, including neural networks (e.g., normalizing flows, deep sets), to accelerate computationally expensive Bayesian inference methods and improve the accuracy of atmospheric models, particularly for complex scenarios like those involving overlapping opacity species in exoplanet atmospheres. These advancements are crucial for enhancing our understanding of exoplanet atmospheres, improving land-use classification from satellite data, and refining climate models. The development of faster and more accurate retrieval methods is driving progress in diverse fields, from astrophysics to remote sensing.