Piras, Davide;
Joachimi, Benjamin;
Villaescusa-Navarro, Francisco;
(2023)
Fast and realistic large-scale structure from machine-learning-augmented random field simulations.
Monthly Notices of the Royal Astronomical Society
, 520
(1)
pp. 668-683.
10.1093/mnras/stad052.
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Abstract
Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cosmological surveys. Many inexpensive substitutes to full N-body simulations have been proposed, even though they often fail to reproduce the statistics of the smaller non-linear scales. Among these alternatives, a common approximation is represented by the lognormal distribution, which comes with its own limitations as well, while being extremely fast to compute even for high-resolution density fields. In this work, we train a generative deep learning model, mainly made of convolutional layers, to transform projected lognormal dark matter density fields to more realistic dark matter maps, as obtained from full N-body simulations. We detail the procedure that we follow to generate highly correlated pairs of lognormal and simulated maps, which we use as our training data, exploiting the information of the Fourier phases. We demonstrate the performance of our model comparing various statistical tests with different field resolutions, redshifts, and cosmological parameters, proving its robustness and explaining its current limitations. When evaluated on 100 test maps, the augmented lognormal random fields reproduce the power spectrum up to wavenumbers of 1hMpc−1, and the bispectrum within 10 per cent, and always within the error bars, of the fiducial target simulations. Finally, we describe how we plan to integrate our proposed model with existing tools to yield more accurate spherical random fields for weak lensing analysis.
| Type: | Article |
|---|---|
| Title: | Fast and realistic large-scale structure from machine-learning-augmented random field simulations |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stad052 |
| Publisher version: | https://doi.org/10.1093/mnras/stad052 |
| Language: | English |
| Additional information: | © The Author(s) 2023. 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/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | methods: statistical, software: simulations, large-scale structure of Universe, dark matter |
| 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/10164360 |
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