Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables
Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables
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DOI：
10.3847/1538-4357/aca8fe
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发表时间：
2022-11
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通讯作者：
Yongseok Jo;S. Genel;Benjamin Dan Wandelt;R. Somerville;F. Villaescusa-Navarro;G. Bryan;D. Anglés-Alcázar;D. Foreman-Mackey;D. Nelson;Ji-hoon Kim
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作者：
Yongseok Jo;S. Genel;Benjamin Dan Wandelt;R. Somerville;F. Villaescusa-Navarro;G. Bryan;D. Anglés-Alcázar;D. Foreman-Mackey;D. Nelson;Ji-hoon Kim
在一种新的方法，采用隐式似然推理（ILI），也被称为无似然推理，我们校准宇宙学流体动力学模拟的参数对观测，这是以前不可行的，由于这些模拟的高计算成本。为了提高计算效率，我们在CAMELS项目的1000个宇宙学模拟上训练神经网络作为仿真器，以估计模拟的观测值，将宇宙学和天体物理学参数作为输入，并使用这些仿真器作为宇宙学模拟的替代品。利用宇宙星星形成率密度（SFRD）和不同红移下的恒星质量函数（SMF），我们对选定的宇宙学和天体物理参数（Ω m，σ 8，星风反馈和动力学黑洞反馈）进行了ILI，得到了完整的六维后验分布。在性能测试中，仿真SFRD（SMF）的ILI可以恢复目标观测值的相对误差为0.17%（0.4%）。我们发现，简并之间存在的参数推断仿真SFRD，证实了新的完整的宇宙学模拟。我们还发现，SMF可以打破SFRD的简并性，这表明SMF为参数提供了补充约束。此外，我们发现，从观测推断SFRD推断的参数组合再现目标观测SFRD非常好，而在SMF的情况下，推断和观察到的SMF显示出显着的差异，表明潜在的限制，目前的星系形成建模和校准框架，和/或系统的差异和观测的SMF之间的不一致。
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 (Ω m , σ 8, 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.