Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
Peter L. Taylor · The Open Journal of Astrophysics · 2025
I show how to compute the nonlinear power spectrum across the entire w ( z ) dynamical dark energy model space. Using synthetic ΛCDM data, I train a neural ordinary differential equation (ODE) to infer the evolution of the nonlinear matter power spectrum as a function of the background expansion and mean matter density across ∼9 Gyr of cosmic evolution. After training, the model generalises to any dynamical dark energy model parameterised by w ( z ) . With little optimisation, the neural ODE is accurate to within 4% up to k = 5 h M p c − 1 . Unlike simulation rescaling methods, neural ODEs naturally extend to summary statistics beyond the power spectrum that are sensitive to the growth history.