Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
https://arxiv.org/html/2408.15136v1 Accessed 2025-06-06
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A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.
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Field-level weak lensing cosmology with $<100$ simulations using multifidelity simulation-based inference
Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.