Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
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Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.
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Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference
Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
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Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference
Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.