English

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI

Cosmology and Nongalactic Astrophysics 2025-07-02 v1 Machine Learning

Abstract

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.

Cite

@article{arxiv.2507.00514,
  title  = {Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI},
  author = {Leander Thiele and Adrian E. Bayer and Naoya Takeishi},
  journal= {arXiv preprint arXiv:2507.00514},
  year   = {2025}
}

Comments

5 pages, 4 figures; accepted at ICML-colocated ML4Astro 2025 workshop

R2 v1 2026-07-01T03:41:04.829Z