{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/psi-gan-a-power-spectrum-informed-generative","title":"Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts","arxiv_id":"2410.07349","date":"2024-10-09","proceeding":null,"authors":["Prabh Bhambra","Benjamin Joachimi","Ofer Lahav","Davide Piras"],"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 \\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 $z \\in [0, 3]$. 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 $1~h~\\mathrm{Mpc}^{-1}$, 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.","url_abs":"https://arxiv.org/abs/2410.07349v3","url_pdf":"https://arxiv.org/pdf/2410.07349v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"psi-gan-a-power-spectrum-informed-generative","repo_url":"https://github.com/prabhbhambra13/psi-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}