Accurate Machine Learning Atmospheric Retrieval via a Neural Network
Surrogate Model for Radiative Transfer
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by
Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atilim Gunes
Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright,
Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
2020
Abstract
Atmospheric retrieval determines the properties of an atmosphere based on its
measured spectrum. The low signal-to-noise ratio of exoplanet observations
require a Bayesian approach to determine posterior probability distributions of
each model parameter, given observed spectra. This inference is computationally
expensive, as it requires many executions of a costly radiative transfer (RT)
simulation for each set of sampled model parameters. Machine learning (ML) has
recently been shown to provide a significant reduction in runtime for
retrievals, mainly by training inverse ML models that predict parameter
distributions, given observed spectra, albeit with reduced posterior accuracy.
Here we present a novel approach to retrieval by training a forward ML
surrogate model that predicts spectra given model parameters, providing a fast
approximate RT simulation that can be used in a conventional Bayesian retrieval
framework without significant loss of accuracy. We demonstrate our method on
the emission spectrum of HD 189733 b and find Bhattacharyya coefficients of
97.74 -- 99.74% between our 1D marginalized posterior distributions and those
of the Bayesian Atmospheric Radiative Transfer (BART) code. Our retrieval
method is ~20x faster than BART when run on an Intel i7-4770 central processing
unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics
processing unit is ~600x faster than BART on that CPU.
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