Issue |
A&A
Volume 681, January 2024
|
|
---|---|---|
Article Number | A3 | |
Number of page(s) | 18 | |
Section | Planets and planetary systems | |
DOI | https://doi.org/10.1051/0004-6361/202346390 | |
Published online | 20 December 2023 |
Parameterizing pressure–temperature profiles of exoplanet atmospheres with neural networks★
1
Max Planck Institute for Intelligent Systems,
Max-Planck-Ring 4,
72076
Tübingen,
Germany
e-mail: tgebhard@tue.mpg.de
2
Max Planck ETH Center for Learning Systems,
Max-Planck-Ring 4,
72076
Tübingen,
Germany
3
ETH Zurich, Institute for Particle Physics & Astrophysics,
Wolfgang-Pauli-Str. 27,
8092
Zurich,
Switzerland
4
Department of Computer Science, ETH Zurich,
8092
Zurich,
Switzerland
Received:
12
March
2023
Accepted:
7
August
2023
Context. Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure–temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters.
Aims. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods.
Methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T.
Results. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three.
Conclusions. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.
Key words: methods: data analysis / methods: statistical / planets and satellites: atmospheres
All code for our method and experiments is available at: https://github.com/timothygebhard/ml4ptp. Our datasets and the final trained models are available here: https://doi.org/10.17617/3.K2CY3M.
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe to Open model.
Open Access funding provided by Max Planck Society.
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