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Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory.

Giovanni Arico'1,2,3, Raul Angulo1,4, Matteo Zennaro1

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We developed a fast neural network emulator to compute the linear matter power spectrum, significantly speeding up cosmological data analysis. This tool achieves high accuracy across various cosmological parameters and redshifts, aiding large-scale structure surveys.

Keywords:
Boltzmann equationsLagrangian Perturbation Theory.cosmological parameterscosmology: theoryemulatorlarge-scale structure of Universe

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Area of Science:

  • Cosmology
  • Astrophysics
  • Computational Science

Background:

  • The linear matter power spectrum is crucial for interpreting large-scale structure observations in cosmology.
  • Current methods using Boltzmann codes (e.g., CLASS, CAMB) are computationally intensive, requiring millions of evaluations for data analysis.
  • This computational cost can be a bottleneck in analyzing cosmological survey data.

Purpose of the Study:

  • To develop a computationally efficient emulator for the linear matter power spectrum.
  • To provide accurate predictions for the linear matter power spectrum across a wide range of cosmological parameters and redshifts.
  • To accelerate the analysis of large-scale structure data for cosmological surveys.

Main Methods:

  • A neural network emulator was trained on over 200,000 cosmological model evaluations.
  • The emulator predicts the linear theory matter power spectrum (total and cold dark matter) with high accuracy.
  • Additional emulators were trained for cross-spectra using 2nd-order Lagrangian Perturbation theory.

Main Results:

  • The emulator computes the linear matter power spectrum in milliseconds with ≈0.2% (0.5%) accuracy up to z ≤ 3 (z ≤ 9).
  • It covers a broad cosmological parameter space, including massive neutrinos and dynamical dark energy.
  • The emulator's accuracy and parameter range are sufficient for unbiased cosmological constraints in Euclid-like weak lensing surveys.

Conclusions:

  • The developed neural network emulator significantly accelerates the computation of the linear matter power spectrum.
  • This tool enhances the efficiency of cosmological data analysis, particularly for large-scale structure surveys.
  • Complementary emulators for related calculations are also available, facilitating comprehensive cosmological modeling.