DEEP LEARNING EXOPLANET ATMOSPHERIC RETRIEVAL WORKFLOW

Need & Challenge

1. Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres.

2. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere%27s temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter.

3. Extraterrestrial environments may have coevolved a broad range of alternative life processes markedly different to those observed on Earth. Can we deploy AI Techniques to generate an extended parameter space for possible metabolisms based on given (observed) environmental conditions and substrates?