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Abstract Recent works have discovered a relatively tight correlation between Ωmand the properties of individual simulated galaxies. Because of this, it has been shown that constraints on Ωmcan be placed using the properties of individual galaxies while accounting for uncertainties in astrophysical processes such as feedback from supernovae and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters (Ωmandσ8) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the Ωminference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on Ωmfrom one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models.
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Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables
Abstract In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ∼1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates for the cosmological simulations. Using the cosmic star formation rate density (SFRD) and, separately, the stellar mass functions (SMFs) at different redshifts, we perform ILI on selected cosmological and astrophysical parameters (Ωm,σ8, stellar wind feedback, and kinetic black hole feedback) and obtain full six-dimensional posterior distributions. In the performance test, the ILI from the emulated SFRD (SMFs) can recover the target observables with a relative error of 0.17% (0.4%). We find that degeneracies exist between the parameters inferred from the emulated SFRD, confirmed with new full cosmological simulations. We also find that the SMFs can break the degeneracy in the SFRD, which indicates that the SMFs provide complementary constraints for the parameters. Further, we find that a parameter combination inferred from an observationally inferred SFRD reproduces the target observed SFRD very well, whereas, in the case of the SMFs, the inferred and observed SMFs show significant discrepancies that indicate potential limitations of the current galaxy formation modeling and calibration framework, and/or systematic differences and inconsistencies between observations of the SMFs.
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- PAR ID:
- 10396828
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 944
- Issue:
- 1
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 67
- Size(s):
- Article No. 67
- Sponsoring Org:
- National Science Foundation
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