Saved in:
Bibliographic Details
Main Author: Taylor, Peter L.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2506.09128
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911115699552256
author Taylor, Peter L.
author_facet Taylor, Peter L.
contents I show how to compute the nonlinear power spectrum across the entire $w(z)$ dynamical dark energy model space. Using synthetic $Λ$CDM 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 $\sim$$9 {\rm \ Gyr}$ of cosmic evolution. After training, the model generalises to {\it any} dynamical dark energy model parameterised by $w(z)$. With little optimisation, the neural ODE is accurate to within $4\%$ up to k = $5 \ h {\rm Mpc}^{-1}$. Unlike simulation rescaling methods, neural ODEs naturally extend to summary statistics beyond the power spectrum that are sensitive to the growth history.
format Preprint
id arxiv_https___arxiv_org_abs_2506_09128
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
Taylor, Peter L.
Cosmology and Nongalactic Astrophysics
I show how to compute the nonlinear power spectrum across the entire $w(z)$ dynamical dark energy model space. Using synthetic $Λ$CDM data, I train a neural ordinary differential equation (ODE) to infer the evolution of the nonlinear matter power spectrum as a function of the background expansion and mean matter density across $\sim$$9 {\rm \ Gyr}$ of cosmic evolution. After training, the model generalises to {\it any} dynamical dark energy model parameterised by $w(z)$. With little optimisation, the neural ODE is accurate to within $4\%$ up to k = $5 \ h {\rm Mpc}^{-1}$. Unlike simulation rescaling methods, neural ODEs naturally extend to summary statistics beyond the power spectrum that are sensitive to the growth history.
title Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
topic Cosmology and Nongalactic Astrophysics
url https://arxiv.org/abs/2506.09128