SBI framework with GNN-on-sets and masked autoregressive flow recovers input cosmologies from eRASS1 mocks at 11.5% precision on Ω_m and 4.4% on σ_8 using 3259 clusters.
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Calibrated NQE enables unbiased field-level cosmological inference from 2D density maps by training mostly on approximate PM simulations and calibrating with ~100 PP simulations.
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Simulation-Based Inference for Cluster Cosmology with Set-Based Neural Network Architectures
SBI framework with GNN-on-sets and masked autoregressive flow recovers input cosmologies from eRASS1 mocks at 11.5% precision on Ω_m and 4.4% on σ_8 using 3259 clusters.
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Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
Calibrated NQE enables unbiased field-level cosmological inference from 2D density maps by training mostly on approximate PM simulations and calibrating with ~100 PP simulations.