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Mapping Saturn using deep learning

Nature Astronomy volume 3, pages 620–625 (2019)Cite this article

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Abstract

Clouds and aerosols provide unique insight into the chemical and physical processes of gas-giant planets. Mapping and characterizing the spectral features indicative of the cloud structure and composition enables an understanding of a planet’s energy budget, chemistry and atmospheric dynamics1,2,3,4. Current space missions to Solar System planets produce high-quality datasets, yet the sheer amount of data obtained often prohibits detailed ‘by hand’ analyses. Current techniques rely mainly on two approaches: identifying the existence of spectral features by dividing the fluxes of two or more spectral channels, and performing full radiative transfer calculations for individual spectra. The first method is not sufficiently accurate and the second is not easily scalable to the entire planetary surface. Here we have developed a deep learning algorithm, PlanetNet, that is able to quickly and accurately map spatial and spectral features across large, heterogeneous areas of a planet. We use PlanetNet to delineate the major components of the 2008 storm on Saturn5, enhancing the scope of the area previously studied and indicating regions that can be probed more deeply with radiative transfer models. Our spectral-component maps indicate compositional and cloud variations of the vast region affected by the storm, showing regions of vertical upwelling, and diminished clouds at the centre of compact substorms.

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Fig. 1: Flowchart of the PlanetNet algorithm.
Fig. 2: Spatial and spectral characteristics of the PlanetNet identified features.
Fig. 3: Cloud distribution as mapped by PlanetNet across six overlapping datasets.
Fig. 4: SR as mapped by PlanetNet across six overlapping datasets.

Data availability

The data analysed in this work are available through the Planetary Data System (https://pds.nasa.gov). In addition, the data used for training PlanetNet are permanently archived and can be accessed with the permanent link: https://osf.io/htgrn or the DOI: https://doi.org/10.17605/OSF.IO/HTGRN.

Code availability

PlanetNet is publicly available through the UCL-Exoplanets GitHub page (https://github.com/ucl-exoplanets/). In addition, the code is permanently archived and can be accessed with the permanent link: https://osf.io/htgrn or the DOI: https://doi.org/10.17605/OSF.IO/HTGRN.

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Acknowledgements

This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 758892, ExoAI) and under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement numbers 617119 (ExoLights). I.P.W. acknowledges funding by the Science and Technology Funding Council (STFC) (grants ST/K502406/1 and ST/P000282/1) and support from Microsoft Azure for Research cloud computing. C.A.G. is funded by the University of Arizona.

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Author notes

  1. These authors contributed equally: I. P. Waldmann, C. A. Griffith.

Authors and Affiliations

  1. Department of Physics and Astronomy, University College London, London, UK

    I. P. Waldmann

  2. University of Arizona, Lunar and Planetary Laboratory, Tucson, AZ, USA

    C. A. Griffith

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  1. I. P. Waldmann
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  2. C. A. Griffith
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Contributions

I.P.W. developed the PlanetNet algorithm and conducted the data analysis. C.A.G. provided the calibrated Cassini/VIMS data and led the spectral interpretation of the data.

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Correspondence to I. P. Waldmann.

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The authors declare no competing interests.

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Journal peer review information: Nature Astronomy thanks Mario D’Amore and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figures 1–14, Supplementary Table 1

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Waldmann, I.P., Griffith, C.A. Mapping Saturn using deep learning. Nat Astron 3, 620–625 (2019). https://doi.org/10.1038/s41550-019-0753-8

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