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Ten-Dimensional Neural Network Emulator for the Nonlinear Matter Power Spectrum.

Yanhui Yang1, Simeon Bird1, Ming-Feng Ho1,2,3

  • 1University of California, Riverside, Department of Physics and Astronomy, 900 University Avenue, Riverside, California 92521, USA.

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This summary is machine-generated.

We developed gokunemu, a fast neural network emulator for the nonlinear matter power spectrum. This tool supports advanced cosmological analyses by accurately predicting power spectra across extended parameter spaces.

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Area of Science:

  • Cosmology
  • Astrophysics
  • Computational Science

Background:

  • The nonlinear matter power spectrum is crucial for cosmological parameter inference.
  • Next-generation surveys require efficient and accurate theoretical predictions.

Purpose of the Study:

  • Introduce gokunemu, a 10D neural network emulator for the nonlinear matter power spectrum.
  • Support next-generation cosmological analyses with rapid and accurate predictions.

Main Methods:

  • Utilized the Goku N-body simulation suite and t2n-muse emulation framework.
  • Developed a 10-dimensional emulator covering parameters beyond Lambda-CDM (ΛCDM).
  • Trained the emulator to predict the matter power spectrum for 0≤z≤3 and 0.006≤k/(h Mpc⁻¹)≤10.

Main Results:

  • Achieved ~0.5% average accuracy in matter power spectrum predictions.
  • Emulator covers extended parameters: dynamical dark energy (w₀, wₐ), massive neutrinos (∑mν), N<0xE2><0x82><0x91><0xE2><0x82><0x91>, and spectral index running (α<0xE2><0x82><0x9B>).
  • Prediction time is ~2 milliseconds per cosmology, orders of magnitude faster than existing emulators.

Conclusions:

  • Gokunemu is the only emulator covering dynamical dark energy models favored by DESI constraints.
  • Its speed and broad parameter coverage make it ideal for upcoming surveys (LSST, Euclid, Roman, CSST).
  • Enables efficient interpretation of complex cosmological data.