论文标题
贝叶斯重力波背景统计的模型依赖性脉冲星定时阵列
Model Dependence of Bayesian Gravitational-Wave Background Statistics for Pulsar Timing Arrays
论文作者
论文摘要
PULSAR时正时阵列（PTA）搜索引力波背景（GWB）通常包括与每个PULSAR固有的时间相关的“红色”噪声模型。使用带有注射GWB信号的简单模拟PTA数据集，我们表明使用的红噪声模型的细节，包括选择振幅先验，甚至PULSAR具有红色噪声，对GWB统计数据产生了惊人的影响，包括GWB振幅的上限和估计值。我们发现，在红噪声振幅上均匀的先验的标准使用导致95％的上限，这是根据单方面的贝叶斯可信间隔计算得出的，其少于注入的GWB振幅50％。另外，GWB的幅度估计值在系统上低于注入的值10-40％，这取决于为固有的红噪声选择哪些模型和先验。我们对模型和先前选择的效果进行分解，并演示了“辍学”模型如何在贝叶斯方法中灵活使用红噪声模型，可以改善GWB的估计。
Pulsar timing array (PTA) searches for a gravitational-wave background (GWB) typically include time-correlated "red" noise models intrinsic to each pulsar. Using a simple simulated PTA dataset with an injected GWB signal we show that the details of the red noise models used, including the choice of amplitude priors and even which pulsars have red noise, have a striking impact on the GWB statistics, including both upper limits and estimates of the GWB amplitude. We find that the standard use of uniform priors on the red noise amplitude leads to 95% upper limits, as calculated from one-sided Bayesian credible intervals, that are less than the injected GWB amplitude 50% of the time. In addition, amplitude estimates of the GWB are systematically lower than the injected value by 10-40%, depending on which models and priors are chosen for the intrinsic red noise. We tally the effects of model and prior choice and demonstrate how a "dropout" model, which allows flexible use of red noise models in a Bayesian approach, can improve GWB estimates throughout.
