噪声邻居：重叠引力波信号的推断偏差
Noisy neighbours: inference biases from overlapping gravitational-wave signals
摘要：
当数据中存在过多信号时，理解和处理引力波（GW）参数估计中的推断偏差是未来GW探测器数据分析的关键挑战之一。在线性信号近似的工作，我们描述了通用的指标来预测GW源参数的推理偏差的存在下，混乱的噪声从不合适的前景，从重叠的信号，合并在时间上接近彼此，并从残差的其他信号，已被不正确地拟合。我们说明了简化的形式主义，但现实的情况下，适合第三代地基（爱因斯坦望远镜）和天基（LISA）探测器，并证明其有效性对蒙特卡罗模拟。我们发现它是一个可靠的工具，可以廉价地预测偏差的程度和方向。最后，我们展示了如何使用这种形式主义来纠正在单个数据集中多个来源的顺序表征中出现的偏差，这可能是在全球拟合分析管道中使用的一个有价值的工具。
英文摘要：
Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working within the linear signal approximation, we describe generic metrics to predict inference biases on GW source parameters in the presence of confusion noise from unfitted foregrounds, from overlapping signals that coalesce close in time to one another, and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simplified, yet realistic, scenarios appropriate to third-generation ground-based (Einstein Telescope) and space-based (LISA) detectors, and demonstrate its validity against Monte-Carlo simulations. We find it to be a reliable tool to cheaply predict the extent and direction of the biases. Finally, we show how this formalism can be used to correct for biases that arise in the sequential characterisation of multiple sources in a single data set, which could be a valuable tool to use within a global-fit analysis pipeline.
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