A transformer-based diffusion model learns the joint distribution of convergence maps and cosmology from log-normal weak lensing simulations and generates calibrated posterior samples matching MCMC results.
Simulation-based inference benchmark for weak lensing cosmology
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The work demonstrates that multi-tracer field-level SBI on galaxy and HI maps yields 2-7 times better constraints on Omega_m and sigma_8 than single-tracer or summary-statistic approaches, with 3D maps performing best.
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
citing papers explorer
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Joint inference of weak lensing convergence map and cosmology with diffusion models
A transformer-based diffusion model learns the joint distribution of convergence maps and cosmology from log-normal weak lensing simulations and generates calibrated posterior samples matching MCMC results.
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Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps
The work demonstrates that multi-tracer field-level SBI on galaxy and HI maps yields 2-7 times better constraints on Omega_m and sigma_8 than single-tracer or summary-statistic approaches, with 3D maps performing best.
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Towards Practical Field-Level Inference for Weak Lensing
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.