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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5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
Amortized neural posterior estimation reproduces nested sampling constraints on RMF couplings for neutron-star EOS with no bias and generates 30,000 samples in 2.5 seconds.
Amortized SBI with spatio-temporal embeddings infers seven CWB parameters from 10-frame Hα time series with well-calibrated posteriors on synthetic data.
Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.
Neural posterior estimation trained on GPU-simulated radar data enables calibrated probabilistic inversion of terrain parameters and transfers to real Mars radar profiles.
citing papers explorer
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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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Amortized Simulation-Based Inference of Relativistic Mean-Field Couplings for Neutron-Star Equations of State
Amortized neural posterior estimation reproduces nested sampling constraints on RMF couplings for neutron-star EOS with no bias and generates 30,000 samples in 2.5 seconds.
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Amortized Simulation-Based Inference of Colliding-Wind Binaries from Short, Noisy Image Time Series
Amortized SBI with spatio-temporal embeddings infers seven CWB parameters from 10-frame Hα time series with well-calibrated posteriors on synthetic data.
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Coverage is not enough: Frequentist tests of simulation-based inference for primordial non-Gaussianity
Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.
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Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
Neural posterior estimation trained on GPU-simulated radar data enables calibrated probabilistic inversion of terrain parameters and transfers to real Mars radar profiles.