Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
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...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Maynooth Academic Publishing
2025-08-01
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| Series: | The Open Journal of Astrophysics |
| Online Access: | https://doi.org/10.33232/001c.143521 |
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| Summary: | 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 <em>any</em> dynamical dark energy model parameterised by $w(z)$. With little optimisation, the neural ODE is accurate to within 4% up to $k = 5\, h\, {\mathrm Mpc}^{−1}$. Unlike simulation rescaling methods, neural ODEs naturally extend to summary statistics beyond the power spectrum that are sensitive to the growth history. |
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| ISSN: | 2565-6120 |