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Painting with baryons: augmenting N-body simulations with gas using deep generative models

Tröster, T, Ferguson, C, Harnois-Déraps, J and McCarthy, IG (2019) Painting with baryons: augmenting N-body simulations with gas using deep generative models. Monthly Notices of the Royal Astronomical Society Letters, 487 (1). L24-L29. ISSN 1745-3933

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Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines-of-sight from SLICS, a suite of N-body simulations optimised for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.

Item Type: Article
Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society Letters ©: 2019 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Uncontrolled Keywords: 0201 Astronomical and Space Sciences
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Divisions: Astrophysics Research Institute
Publisher: Oxford University Press
Related URLs:
Date Deposited: 05 Jul 2019 10:24
Last Modified: 04 Sep 2021 09:13
DOI or ID number: 10.1093/mnrasl/slz075
URI: https://researchonline.ljmu.ac.uk/id/eprint/10984
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