Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators

arXiv:2411.14748v2 Announce Type: replace-cross Abstract: A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce calibrated Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. This approach guarantees an unbiased posterior regardless of approximate simulation accuracy, while achieving near-optimal constraining power when the approximate simulations are reasonably accurate. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dim dark matter density maps up to k_{rm max}sim1.5,h/Mpc at z=0 by training on sim10^4 Particle-Mesh (PM) simulations with transfer function correction and calibrating with sim10^2 Particle-Particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on sim10^4 expensive PP simulations, but at a fraction of the computational cost. Our method offers a practical and scalable framework for SBI of cosmological LSS, enabling precise inference across vast volumes and down to small scales.

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Source: arXiv cs.LG | 2026-04-24