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:2502.13239 , year=
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New CAMELS simulations in larger (50 Mpc/h)^3 boxes with 35 varied parameters produce tighter neural-network constraints on model parameters than prior smaller-volume runs, with public data release.
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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Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
New CAMELS simulations in larger (50 Mpc/h)^3 boxes with 35 varied parameters produce tighter neural-network constraints on model parameters than prior smaller-volume runs, with public data release.