Testing interacting dark energy with Stage IV cosmic shear surveys through differentiable neural emulators

TL;DR

This paper introduces a fast, neural emulator-based framework for forecasting constraints on interacting dark energy models from Stage IV cosmic shear surveys, demonstrating significantly improved precision and efficiency.

Contribution

The authors develop a differentiable neural emulator pipeline for accelerated cosmological inference, enabling precise constraints on dark scattering models from simulated cosmic shear data.

Findings

Stage IV surveys can constrain dark scattering amplitude with an order of magnitude better precision than Stage III.

The neural emulator approach accelerates likelihood calculations by up to 10^5 times.

Constraints remain robust after marginalizing over baryonic feedback and systematics.

Abstract

We employ a novel framework for accelerated cosmological inference, based on neural emulators and gradient-based sampling methods, to forecast constraints on dark energy models from Stage IV cosmic shear surveys. We focus on dark scattering (DS), an interacting dark energy model with pure momentum exchange in the dark sector, and train COSMOPOWER emulators to accurately and efficiently model the DS non-linear matter power spectrum produced by the halo model reaction framework, including the effects of baryon feedback and massive neutrinos. We embed the emulators within a fully-differentiable pipeline for gradient-based cosmological inference for which the batch likelihood call is up to $O(10^5)$ times faster than with traditional approaches, producing parameter constraints from simulated Stage IV cosmic shear data running on a single graphics processing unit (GPU). We also perform model comparison on the output chains from the inference process, employing the learnt harmonic mean estimator implemented in the software HARMONIC. We investigate degeneracies between dark energy and systematics parameters and assess the impact of scale cuts on the final constraints. Assuming a DS model for the mock data vector, we find that a Stage IV survey cosmic shear analysis can constrain the DS amplitude parameter $A_{\mathrm{ds}}$ with an uncertainty roughly an order of magnitude smaller than current constraints from Stage III surveys, even after marginalising over baryonic feedback, intrinsic alignments and redshift distribution uncertainties. These results show great promise for constraining DS with Stage IV data; furthermore, our methodology can be straightforwardly extended to a wide range of dark energy and modified gravity models.