Fast production of cosmological emulators in modified gravity: the matter power spectrum

JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS(2023)

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摘要
We test the convergence of fast simulations based on the COmoving Lagrangian Acceleration (COLA) method for predictions of the matter power spectrum, specialising our analysis in the redshift range 1 <= z <= 1.65, relevant to high-redshift spectroscopic galaxy surveys. We then focus on the enhancement of the matter power spectrum in modified gravity (MG), the boost factor, using the Dvali-Gabadadze-Porrati (DGP) theory as a test case but developing a general approach that can be applied to other MG theories. After identifying the minimal simulation requirements for accurate DGP boost factors, we design and produce a COLA simulation suite that we use to train a neural network emulator for the DGP boost factor. Using MG-AREPO simulations as a reference, we estimate the emulator accuracy to be of similar to 3% up to k = 5 h Mpc-1 at 0 <= z <= 2. We make the emulator publicly available at: https://github.com/BartolomeoF/nDGPemu.
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关键词
modified gravity,power spectrum,cosmological simulations,Machine learning
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