Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary a...

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Bibliographic Details
Published in:arXiv.org 2018-12
Main Authors: Soboczenski, Frank, Himes, Michael D, O'Beirne, Molly D, Zorzan, Simone, Baydin, Atilim Gunes, Cobb, Adam D, Yarin Gal, Angerhausen, Daniel, Mascaro, Massimo, Arney, Giada N, Domagal-Goldman, Shawn D
Format: Article
Language:English
Subjects:
Atmospheric composition
Atmospheric models
Bayesian analysis
Biological activity
Computer vision
Datasets
Deep learning
Extrasolar planets
Fluxes
Machine learning
Parameter uncertainty
Planet detection
Planetary atmospheres
Planetary evolution
Retrieval
Telescopes
Terrestrial planets
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Summary:Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.
ISSN:2331-8422