Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

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 good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843--0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90--180x faster per chain than BART on that CPU.

arXiv comment16 pages, 4 figures, submitted to PSJ 3/4/2020, revised 1/22/2021, accepted 2/4/2021, published 4/25/2022. Updated to match the published manuscript. Himes et al. 2022, PSJ, 3, 91

References in corpus (26)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• The frontier of simulation-based inference
• Sequence Level Training with Recurrent Neural Networks
• A high accuracy computed water line list
• Exoplanet Atmospheres
• A Systematic Retrieval Analysis of Secondary Eclipse Spectra I: A Comparison of Atmospheric Retrieval Techniques
• The climate of HD 189733b from fourteen transits and eclipses measured by Spitzer
• Planetary Spectrum Generator: an accurate online radiative transfer suite for atmospheres, comets, small bodies and exoplanets
• H- Opacity and Water Dissociation in the Dayside Atmosphere of the Very Hot Gas Giant WASP-18 b
• The Broadband Infrared Emission Spectrum of the Exoplanet HD 189733b
• Strong Water Absorption in the Dayside Emission Spectrum of the Planet HD 189733b
• An accurate, extensive, and practical line list of methane for the HITEMP database
• A Guide to Constraining Effective Field Theories with Machine Learning
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Characterizing Earth Analogs in Reflected Light: Atmospheric Retrieval Studies for Future Space Telescopes
• Exploring Biases of Atmospheric Retrievals in Simulated JWST Transmission Spectra of Hot Jupiters
• Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
• ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks
• An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
• Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification
• Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
• Building high accuracy emulators for scientific simulations with deep neural architecture search
• Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
• Mapping Saturn using deep learning
• Multilayer Perceptron and Geometric Albedo Spectra for Quick Parameter Estimations of Exoplanets
• Probabilistic Surrogate Networks for Simulators with Unbounded Randomness

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• Neural posterior estimation for exoplanetary atmospheric retrieval
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• Grid-Based Atmospheric Retrievals for Reflected-Light Spectra of Exoplanets using PSGnest
• Enhancing 3D Planetary Atmosphere Simulations with a Surrogate Radiative Transfer Model
• Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
• Radiative Transfer and Inversion codes for characterizing planetary atmospheres: an overview
• Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra
• A normalizing flow approach for the inference of star cluster properties from unresolved broadband photometry I: Comparison to spectral energy distribution fitting
• $\text{Exoformer}$: Accelerating Bayesian atmospheric retrievals with transformer neural networks