Papers › Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of...
Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
Prabh Bhambra, Benjamin Joachimi, Ofer Lahav, Davide Piras
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Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. N-body simulations are computationally expensive, and many cheaper alternatives (such as lognormal random fields) fail to reproduce accurate statistics of the smaller, non-linear scales. In this work, we present \textsc{Psi-GAN} (\textbf{P}ower-\textbf{s}pectrum-\textbf{i}nformed \textbf{G}enerative \textbf{A}dversarial \textbf{N}etwork), a machine learning model which takes a two-dimensional lognormal dark matter density field and transforms it into a more realistic field. We construct \textsc{Psi-GAN} so that it is continuously conditional, and can therefore generate realistic realisations of the dark matter density field across a range of cosmologies and redshifts in z ∈[0, 3]. We train \textsc{Psi-GAN} as a generative adversarial network on 2 000 simulation boxes from the Quijote simulation suite. We use a novel critic architecture that utilises the power spectrum as the basis for discrimination between real and generated samples. \textsc{Psi-GAN} shows agreement with N-body simulations over a range of redshifts and cosmologies, consistently outperforming the lognormal approximation on all tests of non-linear structure, such as being able to reproduce both the power spectrum up to wavenumbers of 1 h Mpc⁻¹, and the bispectra of target N-body simulations to within ∼5 per cent. Our improved ability to model non-linear structure should allow more robust constraints on cosmological parameters when used in techniques such as simulation-based inference.
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