Papers › Power Spectrum Emulators from Neural Networks and Tree-Based Methods
Power Spectrum Emulators from Neural Networks and Tree-Based Methods
Andrei Lazanu
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We use two subsets of 2000 and 1000 Quijote simulations to build two power spectrum emulators, allowing for fast computations of the non-linear matter power spectrum. The first emulator is built in terms of seven cosmological parameters: the matter and baryon fraction of the energy density of the Universe Ωₘ and Ω_b, the reduced Hubble constant h, the scalar spectral index nₛ, the amplitude of matter density fluctuations σ₈, the total neutrino mass M_ν and the dark energy equation of state parameter w, on scales k ∈[0.015,1.8] h/ Mpc⁻¹. The power spectra can be directly determined at redshifts 0, 0.5, 1, 2 and 3, while for intermediate redshifts these can be interpolated. The second emulator is based on five cosmological parameters, Ωₘ, h, nₛ, σ₈ and the amplitude of equilateral non-Gaussianity f_(NL)^(eq), at redshifts 0, 0.503, 0.733, 0.997 for k ∈[0.015,1.8] h/ Mpc⁻¹. The emulators are built on machine learning techniques. In both cases we have investigated both neural networks and tree-based methods and we have shown that the best accuracy is obtained for a neural network with two hidden layers. Both emulators achieve a root-mean-squared relative error of less then 5\% for all the redshifts considered on the scales discussed.
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