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Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
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abstract
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The model is trained on ground truth distributions for seven parameters: the planet radius, the atmospheric temperature, and the mixing ratios for five common absorbers: $H_2O$, $CH_4$, $NH_3$, $CO$ and $CO_2$. The model performance is enhanced by domain-inspired preprocessing of the features and the use of semi-supervised learning in order to leverage the large amount of unlabelled training data available. The model was among the winning solutions in the 2023 Ariel Machine Learning Data Challenge.
Forward citations
Cited by 2 Pith papers
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Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
On a synthetic JWST/Ariel-style spectral database, XGBoost and SVM with per-spectrum normalization and log-abundance targets outperform random forests and other classical regressors for exoplanet atmospheric retrievals.
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Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
ELM surrogates can replace gradient-trained deep networks for low-dimensional sequential radiative transfer emulation with far less training time and data, while the image mapping task needs a 50-model ensemble that g...
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