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On the road to percent accuracy: nonlinear reaction of the matter power spectrum to dark energy and modified gravity
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abstract
We present a general method to compute the nonlinear matter power spectrum for dark energy and modified gravity scenarios with percent-level accuracy. By adopting the halo model and nonlinear perturbation theory, we predict the reaction of a $\Lambda$CDM matter power spectrum to the physics of an extended cosmological parameter space. By comparing our predictions to $N$-body simulations we demonstrate that with no-free parameters we can recover the nonlinear matter power spectrum for a wide range of different $w_0$-$w_a$ dark energy models to better than 1% accuracy out to $k \approx 1 \, h \, {\rm Mpc}^{-1}$. We obtain a similar performance for both DGP and $f(R)$ gravity, with the nonlinear matter power spectrum predicted to better than 3% accuracy over the same range of scales. When including direct measurements of the halo mass function from the simulations, this accuracy improves to 1%. With a single suite of standard $\Lambda$CDM $N$-body simulations, our methodology provides a direct route to constrain a wide range of non-standard extensions to the concordance cosmology in the high signal-to-noise nonlinear regime.
Forward citations
Cited by 4 Pith papers
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Emulating the nonlinear effects of modified gravity on the matter power spectrum for reconstruction
A neural-network emulator predicts the ratio of nonlinear to linear modified-gravity matter power spectra across a 28-dimensional cosmological and MG parameter space, matching MGCAMB+ReACT to roughly 1–2%.
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Disentangling modified gravity and galaxy bias with field-level inference
With fixed known initial phases, voxel-by-voxel Poisson likelihood on the galaxy number-counts field breaks the f(R)–bias degeneracy that power spectra cannot resolve, with voids and walls driving the gain.
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Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
A neural ODE trained only on LambdaCDM spectra predicts nonlinear matter power spectra to about 4 percent accuracy for smooth w(z) dark energy models, pending stronger validation.
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Extending CSST Emulator to post-DESI era
A tuned 'spectral equivalence' mapping lets the CSST emulator predict nonlinear matter power spectra at ~1% accuracy across the DESI DR2+CMB dynamic-dark-energy posterior.
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