[论文解读] FLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning

Roi Kugel, Joop Schaye+18 more|arXiv (Cornell University)|Jun 8, 2023

Galaxies: Formation, Evolution, Phenomena被引用 7

一句话总结

论文使用在较小 FLAMINGO 运行的拉丁超立方体上训练的高斯过程仿真器来校准星系风与黑洞反馈子网格模型，将子网格参数与可观测量（如恒星质量函数和团簇气体分数）在三个 FLAMINGO 分辨率下关联起来。

ABSTRACT

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 cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller-volume simulations, we model how the galaxy stellar mass function 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 stellar mass function. 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.

研究动机与目标

• 通过校准子网格模型量化 baryonic 物理对宇宙学可观测量的影响。
• 开发以仿真器为基础的校准，避免人工调整并实现对子网格参数的 MCMC 采样。
• 在三个 FLAMINGO 分辨率上应用该方法以评估鲁棒性和预测能力。
• 探索受观测数据与不确定性约束的模型变体空间。
• 提供一个框架，从已校准的模拟中推导出 baryonic 效应的上下限。

提出的方法

• 在 32 个小体积模拟的拉丁超立方抽样上训练高斯过程仿真器，用以建模 SMF 和团簇气体分数如何随子网格参数变化。
• 使用仿真器输出进行 MCMC 拟合观测数据，同时考虑统计与系统偏差。
• 在三个分辨率下，对恒星反馈、BH 增长和 AGN 反馈的子网格参数进行校准，先验由物理因素提供信息。
• 使用 Swift 代码，结合来自 OWLS/BAHAMAS/EAGLE 家族的子网格规定，采用 SPH、随机恒星形成以及动力/热反馈方案。
• 研究子网格参数的改变如何映射到可观测量，并识别落在观测允许范围内或超出范围的模型变体。
• 提供一个数据驱动、以观测量为中心的模型变体描述，而非以参数为中心的描述。

实验结果

主要发现

• 基于仿真器的方法在考虑观测偏差的情况下，能够将子网格物理与 SMF 和气体分数拟合。
• 该方法在三种 FLAMINGO 分辨率的可分辨质量范围内再现观测关系。
• 仿真器揭示出在团簇气体分数与 SMF 方面游离于观测允许范围之外的模型变体。
• 子网格参数可通过数据校准的变体来解释，而非固定参数值，从而提升对 baryonic 效应的预测能力。
• 该框架通过在各分辨率上探索已校准参数空间，提供对 baryonic 效应的上下限。