Parametrized models that predict the gravitational-wave (GW) signal from merging black holes are used to extract source properties from GW observations. The majority of research in this area has focused on developing methods capable of producing highly accurate, point estimate, predictions for the GW signal. A key element missing from every model used in the analysis of GW data is an estimate for how confident the model is in its prediction. This omission increases the risk of biased parameter estimation of source properties. Current strategies include running analyses with multiple models to measure systematic bias however, this fails to accurately reflect the true uncertainty in the models. In this work we develop a probabilistic extension to the phenomenological modeling workflow for nonspinning black holes and demonstrate that the model not only produces accurate point estimates for the GW signal but can be used to provide well-calibrated local estimates for its uncertainty. Our analysis highlights that there is a lack of numerical relativity (NR) simulations available at multiple resolutions which can be used to estimate their numerical error and implore the NR community to continue to improve their estimates for the error in NR solutions published. Waveform models that are not only accurate in their point-estimate predictions but also in their error estimates are a potential way to mitigate bias in GW parameter estimation of compact binaries due to unconfident waveform model extrapolations.

中文翻译：

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黑洞合并引力波信号的概率模型

预测合并黑洞产生的引力波 (GW) 信号的参数化模型用于从 GW 观测中提取源属性。该领域的大部分研究都集中在开发能够对引力波信号进行高精度点估计和预测的方法。 GW 数据分析中使用的每个模型都缺少一个关键要素，即对模型预测的置信度的估计。这种遗漏增加了源属性参数估计有偏差的风险。当前的策略包括使用多个模型进行分析来测量系统偏差，但这无法准确反映模型中真正的不确定性。在这项工作中，我们开发了非旋转黑洞现象学建模工作流程的概率扩展，并证明该模型不仅可以为引力波信号生成准确的点估计，而且可以用于为其不确定性提供经过良好校准的局部估计。我们的分析强调，缺乏多种分辨率下可用的数值相对论 (NR) 模拟，可用于估计其数值误差，并恳请 NR 界继续改进对已发布的 NR 解决方案中误差的估计。波形模型不仅在点估计预测方面准确，而且在误差估计方面也准确，是减轻由于不自信的波形模型外推而导致的紧凑二进制文件的引力波参数估计偏差的潜在方法。