An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Cobb, Adam D.; Himes, Michael D.; Soboczenski, Frank; Zorzan, Simone; O’Beirne, Molly D.; Güneş Baydin, Atılım; Gal, Yarin; Domagal-Goldman, Shawn D.; Arney, Giada N.; Angerhausen, Daniel (2019). An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. The astronomical journal, 158(1), p. 33. American Astronomical Society 10.3847/1538-3881/ab2390

1905.10659.pdf - Draft Version
Available under License Publisher holds Copyright.

Download (7MB) | Preview
[img] Text
Cobb_2019_AJ_158_33.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (2MB) | Request a copy

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, plan-net, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Physics Institute > Space Research and Planetary Sciences
08 Faculty of Science > Physics Institute
10 Strategic Research Centers > Center for Space and Habitability (CSH)
08 Faculty of Science > Physics Institute > NCCR PlanetS

UniBE Contributor:

Angerhausen, Daniel


500 Science > 520 Astronomy
500 Science > 530 Physics




American Astronomical Society




Danielle Zemp

Date Deposited:

14 Apr 2020 10:16

Last Modified:

05 Dec 2022 15:38

Publisher DOI:


ArXiv ID:





Actions (login required)

Edit item Edit item
Provide Feedback