Unsupervised domain adaptation lets a neural network trained on one cosmological simulation recover the matter density from unlabeled HI maps of a different simulation with R² ≥ 0.9.
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Towards cosmological inference on unlabeled out-of-distribution HI observational data
Unsupervised domain adaptation lets a neural network trained on one cosmological simulation recover the matter density from unlabeled HI maps of a different simulation with R² ≥ 0.9.