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Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles
Timothy Gebhard · Daniel Angerhausen · Björn Konrad · Eleonora Alei · Sascha Quanz · Bernhard Schölkopf

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in Machine Learning and the Physical Sciences »

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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Author Information

Timothy Gebhard (Max Planck Institute for Intelligent Systems, Tübingen)
Daniel Angerhausen (ETH Zürich)
Björn Konrad (ETH Zürich)
Eleonora Alei (ETH Zürich)
Sascha Quanz (ETH Zürich)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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