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

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 them within a fully differentiable pipeline for gradient-based cosmological inference for which the batch likelihood call is up to $\mathcal {O}(10^5)$ times faster than with traditional approaches, producing constraints from simulated Stage IV cosmic shear data running on a single graphics processing unit. 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 marginalizing 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.