Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
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| Format: | Preprint |
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2024
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| _version_ | 1866916551939063808 |
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| author | Bhambra, Prabh Joachimi, Benjamin Lahav, Ofer Piras, Davide |
| author_facet | Bhambra, Prabh Joachimi, Benjamin Lahav, Ofer Piras, Davide |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_07349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts Bhambra, Prabh Joachimi, Benjamin Lahav, Ofer Piras, Davide Cosmology and Nongalactic Astrophysics 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. |
| title | Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2410.07349 |