Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI

Thiele, Leander, Bayer, Adrian E., Takeishi, Naoya

arXiv.org Artificial Intelligence 

The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inference based on feature matching and knowledge distillation. Our method results in improved posterior quality, particularly for small simulation budgets and difficult inference problems.

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