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.
arXiv preprint arXiv:2211.00660 , year=
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AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
citing papers explorer
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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 Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.