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FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning.

Kugel, Roi; Schaye, Joop; Schaller, Matthieu; Helly, John C; Braspenning, Joey; Elbers, Willem; Frenk, Carlos S; McCarthy, Ian G; Kwan, Juliana; Salcido, Jaime; van Daalen, Marcel P; Vandenbroucke, Bert; Bahé, Yannick M; Borrow, Josh; Chaikin, Evgenii; Huško, Filip; Jenkins, Adrian; Lacey, Cedric G; Nobels, Folkert S J; Vernon, Ian

FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning. Thumbnail


Roi Kugel

Joop Schaye

John C Helly

Joey Braspenning

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Willem Elbers
Postdoctoral Research Associate

Carlos S Frenk

Ian G McCarthy

Juliana Kwan

Jaime Salcido

Marcel P van Daalen

Bert Vandenbroucke

Yannick M Bahé

Josh Borrow

Evgenii Chaikin

Filip Huško

Folkert S J Nobels


To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO (Fullhydro Large-scale structure simulations with All-sky Mapping for the Interpretation of Next Generation Observations) cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller volume simulations, we model how the galaxy stellar mass function (SMF) and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the SMF. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters. [Abstract copyright: © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.]

Journal Article Type Article
Acceptance Date Aug 12, 2023
Online Publication Date Oct 5, 2023
Publication Date 2023-12
Deposit Date Dec 19, 2023
Publicly Available Date Dec 19, 2023
Journal Monthly notices of the Royal Astronomical Society
Print ISSN 0035-8711
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 526
Issue 4
Pages 6103-6127
Keywords large-scale structure of Universe, galaxies: formation, galaxies: clusters: general, cosmology: theory, methods: statistical, methods: numerical
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Copyright Statement
This article has been accepted for publication in Monthly notices of the Royal Astronomical Society: 2023 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

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