Psi-GAN: a power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts.

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 Psi-GAN (power-spectrum-informed generative adversarial network), a machine learning model that takes a two-dimensional lognormal dark matter density field and transforms it into a more realistic field. We construct Psi-GAN so that it is continuously conditional, and can therefore generate realistic realizations of the dark matter density field across a range of cosmologies and redshifts in |$z \in [0, 3]$|⁠. We train Psi-GAN as a generative adversarial network on |$2\, 000$| simulation boxes from the Quijote simulation suite. We use a novel critic architecture that utilizes the power spectrum as the basis for discrimination between real and generated samples. 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 |$1~h~\mathrm{Mpc}^{-1}$|⁠ , and the bispectra of target N -body simulations to within |${\sim }5$|  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. [ABSTRACT FROM AUTHOR]