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Workshop: Machine Learning and the Physical Sciences

Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles

Timothy Gebhard · Daniel Angerhausen · Björn Konrad · Eleonora Alei · Sascha Quanz · Bernhard Schölkopf


We describe a new, learning-based approach for parameterizing the relationship between pressure and temperature in the atmosphere of an exoplanet. Our method can be used, for example, when estimating the parameters characterizing a planet's atmosphere from an observation of its spectrum with Bayesian inference methods (“atmospheric retrieval”). On two data sets, we show that our method requires fewer parameters and achieves, on average, better reconstruction quality than existing methods, all while still integrating easily into existing retrieval frameworks. This may help the analysis of exoplanet observations as well as the design of future instruments by speeding up inference, freeing up resources to retrieve more parameters, and paving a way to using more realistic atmospheric models for retrievals.

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