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Related Experiment Videos

Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators.

¹Princeton University, Department of Astrophysical Sciences, Princeton, New Jersey 08544, USA.

Physical Review Letters

|May 11, 2026

Summary

We developed a new method for cosmological parameter inference using approximate simulations for training and high-fidelity simulations for calibration. This approach efficiently extracts information from large-scale structure surveys, reducing computational costs.

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Area of Science:

Cosmology
Astrophysics
Computational Science

Background:

Cosmological large-scale structure (LSS) surveys require high-fidelity simulations for accurate parameter inference.
Generating these simulations is computationally expensive, posing a significant challenge for data analysis.

Purpose of the Study:

To introduce a novel simulation-based inference (SBI) method called calibrated neural quantile estimation.
To enable efficient and accurate cosmological parameter inference from LSS data despite simulation limitations.

Main Methods:

Leveraging a large number of approximate simulations for initial training.
Utilizing a small set of high-fidelity simulations for calibration to ensure unbiased posteriors.
Applying the method to infer cosmological parameters from 2D dark matter density maps.

Main Results:

The method achieves near-optimal constraining power, even with approximate simulations.
Cosmological parameters were inferred at field level up to k_max ~ 1.5 h/Mpc at z=0.
Calibrated posteriors closely matched those from training on expensive simulations, but at a fraction of the cost.

Conclusions:

Calibrated neural quantile estimation provides a practical and scalable framework for SBI in cosmology.
This method significantly reduces the computational cost of parameter inference for LSS surveys.
Enables precise inference across vast cosmological volumes and down to smaller scales.