使用贝叶斯归一化流模拟紧致双星族合成模拟并进行不确定性量化与模型比较
Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows
AI总结：
本研究提出基于贝叶斯归一化流的紧致双星族合成模拟方法，可量化并边缘化模拟不确定性，在双黑洞族模拟中验证了效果，可应用于引力波星表推断与特征预测场景。
摘要：
紧致双星并合（CBCs）的族合成模拟在从引力波（GW）观测集合中提取天体物理学洞见方面发挥着关键作用。然而，针对密集的初始条件网格开展真实模拟的计算成本可能很高。归一化流可以模拟族合成运行，从而实现基于模拟的、从观测星表出发的推断，以及为罕见可合成子群中的特征预测进行数据增强。但归一化流的预测可能存在不确定性，尤其是在训练集稀疏的情况下。\n在本研究中，我们通过实现贝叶斯归一化流（一种由贝叶斯神经网络构建的条件密度估计器），开发了一种对模拟器中的不确定性进行量化并边缘化的方法。利用与密度估计器天然关联的精确似然函数，我们通过合理选择的先验采样流参数的后验分布，以量化并边缘化流的不确定性。我们针对通过公共包层演化形成的双黑洞族模拟，验证了所估计不确定性的准确性、校准性、对推断的影响以及对数据增强的影响。我们概述了所提方法在基于不断增长的GW星表进行模拟推断、以及结合前沿双星演化模拟器开展特征预测场景下的应用，目前该方法已实现对模型和数据不确定性的边缘化。
英文摘要：
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations can be costly to implement for a dense grid of initial conditions. Normalizing flows can emulate population synthesis runs to enable simulation-based inference from observed catalogs and data augmentation for feature prediction in rarely synthesizable sub-populations. However, flow predictions can be wrought with uncertainties, especially for sparse training sets. In this work, we develop a method for quantifying and marginalizing uncertainties in the emulators by implementing the Bayesian Normalizing flow, a conditional density estimator constructed from Bayesian neural networks. Using the exact likelihood function naturally associated with density estimators, we sample the posterior distribution of flow parameters with suitably chosen priors to quantify and marginalize over flow uncertainties. We demonstrate the accuracy, calibration, inference, and data-augmentation impacts of the estimated uncertainties for simulations of binary black hole populations formed through common envelope evolution. We outline the applications of the proposed methodology in the context of simulation-based inference from growing GW catalogs and feature prediction, with state-of-the-art binary evolution simulators, now marginalized over model and data uncertainties.
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