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MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) generates exoplanet spectra across a defined parameter space, processes the output, and trains, validates, and tests machine learning models as a fast approximation to radiative transfer. It uses BART (ascl:1608.004) for spectra generation and modifies BART’s Bayesian sampler (MC3, ascl:1610.013) with a random uniform sampler to propose models within a defined parameter space. More generally, MARGE provides a framework for training neural network models to approximate a forward, deterministic process.
plan-net uses machine learning with an ensemble of Bayesian neural networks for atmospheric retrieval; this approach yields greater accuracy and more robust uncertainties than a single model. A new loss function for BNNs learns correlations between the model outputs. Performance is improved by incorporating domain-specific knowledge into the machine learning models and provides additional insight by inferring the covariance of the retrieved atmospheric parameters.