BibTeX

@article{2411.14748v1,
Author        = {He Jia},
Title         = {Cosmological Analysis with Calibrated Neural Quantile Estimation and
  Approximate Simulators},
Eprint        = {2411.14748v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
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 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 and achieves 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.},
Year          = {2024},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2411.14748v1},
File          = {2411.14748v1.pdf}
}