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FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning
Roi Kugel1, Joop Schaye1, Matthieu Schaller1,2
1Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands.
Monthly Notices of the Royal Astronomical Society
|October 30, 2023
Summary
Machine learning calibrates baryonic feedback models in cosmological simulations, improving predictions for galaxy stellar mass and cluster gas fractions. This method links simulation parameters to observable data for better accuracy.
Area of Science:
- Cosmology
- Astrophysics
- Computational Science
Background:
- Baryonic effects, including active galactic nuclei (AGN) and star formation feedback, significantly impact cosmological observables.
- These feedback processes occur on sub-grid scales in simulations, requiring parameterization through subgrid models.
Purpose of the Study:
- To calibrate AGN and stellar feedback models in cosmological hydrodynamical simulations using machine learning.
- To quantify the impact of subgrid parameters on galaxy stellar mass functions (SMF) and cluster gas fractions.
Main Methods:
- Utilized Gaussian process emulators trained on smaller volume FLAMINGO simulations.
- Modeled the relationship between subgrid parameters and cosmological observables (SMF, cluster gas fractions).
- Fitted emulators to observational data, incorporating potential observational biases.
Main Results:
- Successfully recovered observed relations within resolved mass ranges across different FLAMINGO simulation resolutions.
- Identified models that approach or exceed observationally allowed ranges for cluster gas fractions and SMF.
- Demonstrated the ability to link subgrid parameter variations to specific observational data.
Conclusions:
- The machine learning approach effectively calibrates baryonic feedback models in cosmological simulations.
- This method allows for defining model variations based on observational calibration rather than arbitrary subgrid parameter values.
- The approach is valuable due to the complex interplay between multiple subgrid parameters and specific observables.
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