Bhambra, Prabh;
Joachimi, Benjamin;
Lahav, Ofer;
Piras, Davide;
(2025)
Psi-GAN: a power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts.
Monthly Notices of the Royal Astronomical Society
, 536
(3)
pp. 3138-3157.
10.1093/mnras/stae2810.
Preview |
Text
Joachimi_Psi-GAN- a power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts_VoR.pdf Download (3MB) | Preview |
Abstract
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 ∈ [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 1h 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.
| Type: | Article |
|---|---|
| Title: | Psi-GAN: a power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stae2810 |
| Publisher version: | https://doi.org/10.1093/mnras/stae2810 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | methods: statistical, software: simulations, (cosmology:) dark matter, (cosmology:) large-scale structure of Universe |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204340 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
