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.
What is an equivariant neural network?
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We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision to AlphaFold 2 for protein structure prediction, without assuming knowledge of equivariance or neural networks. The basic mathematical ideas are simple but are often obscured by engineering complications that come with practical realizations. We extract and focus on the mathematical aspects, and limit ourselves to a cursory treatment of the engineering issues at the end.
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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.