标题： 将模型不确定性纳入引力波参数估计的新方法 显示英文标题

标题： Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates

摘要： 使用贝叶斯技术从实验数据计算得到的参数后验分布，其准确性仅取决于用于构建它们的模型。在许多应用中，这些模型是不完整的，这不仅降低了检测的可能性，还会导致参数估计中的系统误差。例如，在引力波探测器数据的分析中，可以使用数值方法计算准确的波形模板，但这些模拟的高昂成本意味着只能对少量参数进行计算。在这项工作中，提出了一种将模型不确定性纳入数据分析的新方法；通过使用高斯过程回归来插值来自少量准确模板的波形差异，构建先验分布，并对该波形不确定性进行解析边缘化。该方法有充分的动机，易于实现，并且计算成本与标准技术相当。当应用于一个简单的示例问题时，新方法表现出色。虽然我们使用引力波数据分析的应用来激发和说明该技术，但它可以在任何存在模型不确定性的场景中应用。 显示英文摘要

摘要： Posterior distributions on parameters computed from experimental data using Bayesian techniques are only as accurate as the models used to construct them. In many applications these models are incomplete, which both reduces the prospects of detection and leads to a systematic error in the parameter estimates. In the analysis of data from gravitational wave detectors, for example, accurate waveform templates can be computed using numerical methods, but the prohibitive cost of these simulations means this can only be done for a small handful of parameters. In this work a novel method to fold model uncertainties into data analysis is proposed; the waveform uncertainty is analytically marginalised over using with a prior distribution constructed by using Gaussian process regression to interpolate the waveform difference from a small training set of accurate templates. The method is well motivated, easy to implement, and no more computationally expensive than standard techniques. The new method is shown to perform extremely well when applied to a toy problem. While we use the application to gravitational wave data analysis to motivate and illustrate the technique, it can be applied in any context where model uncertainties exist.