arXiv Machine Learning
Three-dimensional Conditional Diffusion Models for Cosmological 21 cm Lightcone Emulation
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21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables
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Cross-simulator transfer with foundation model summaries: Towards robust SKA-era reionization inference
The paper demonstrates that a self‑supervised Vision Transformer (ViT) pretrained on a fast, low‑cost semi‑numerical simulator can produce data summaries that transfer across different simulators without retraining. In 21cm cosmology, the ViT—named SKATR—pretrained on 67,000 21cmFAST lightcones is applied unchanged to hydrodynamical Loreli II lightcones, enabling accurate inference of five astrophysical parameters with fewer radiative‑transfer simulations than a fully‑supervised baseline. SKATR remains accurate, informative, and calibrated even under realistic SKA antenna array noise, outperforming supervised models retrained on noisy data.
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Efficient Confidence-Aware Densification for 3DGS
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