FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning.

Autor:	Kugel R; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Schaye J; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Schaller M; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands.; Lorentz Institute for Theoretical Physics, Leiden University, PO box 9506, NL-2300 RA Leiden, the Netherlands., Helly JC; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., Braspenning J; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Elbers W; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., Frenk CS; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., McCarthy IG; Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK., Kwan J; Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK., Salcido J; Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK., van Daalen MP; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Vandenbroucke B; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Bahé YM; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands.; Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, CH-1290 Versoix, Switzerland., Borrow J; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK.; Department of Physics, Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Chaikin E; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Huško F; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., Jenkins A; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., Lacey CG; Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK., Nobels FSJ; Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands., Vernon I; Department of Mathematical Sciences, Durham University, Stockton Road, DH1 3LE Durham, UK.
Jazyk:	angličtina
Zdroj:	Monthly notices of the Royal Astronomical Society [Mon Not R Astron Soc] 2023 Oct 05; Vol. 526 (4), pp. 6103-6127. Date of Electronic Publication: 2023 Oct 05 (Print Publication: 2023).
DOI:	10.1093/mnras/stad2540
Abstrakt:	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. (© 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.)
Databáze:	MEDLINE
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