Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators

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, 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-dimensional dark matter density maps up to k_{max}∼1.5  h/Mpc at z=0 by training on ∼10^{4} particle-mesh simulations with transfer function correction and calibrating with ∼10^{2} particle-particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on ∼10^{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.