一种用于脉冲星计时阵列数据随机引力波背景多分辨率图的延迟拒绝可逆跳跃马尔可夫链蒙特卡罗方法
A Delayed Rejection Reversible Jump Markov Chain Monte Carlo Method for Multi-Resolution Maps of the Stochastic Gravitational Wave Background with Pulsar Timing Array Data
- Texas Tech University（德克萨斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对脉冲星计时阵列随机引力波背景图分辨率设定，提出延迟拒绝可逆跳跃采样器及频率学派方法，减少参数数量并避免过拟合，实现数据驱动的多分辨率推断。
AI中文摘要：
脉冲星计时阵列各向异性分析通常使用朴素的计数论证来设定推断的随机引力波背景（GWB）图的分辨率。我们提出了一种数据驱动的方法，采用延迟拒绝可逆跳跃采样器，专门针对GWB角功率密度的多分辨率像素分解。我们还考虑了一种基于信息准则统计的快速频率学派替代方法。我们通过一系列注入-恢复模拟验证了我们的方法，发现标准的计数论证会导致数据的严重过拟合，而我们的数据驱动方法将模型中的参数数量减少了一到三个数量级，并且在数据支持的情况下，能够实现比标准计数论证更高的分辨率。我们将我们的采样器和频率学派方法实现公开在GitHub上。
英文摘要：
Pulsar timing array anisotropy analyses often use a naive counting argument to set resolutions of inferred maps of the stochastic gravitational wave background (GWB). We present a data-driven method in the form of a delayed rejection reversible jump sampler tailored to multi-resolution pixel decompositions of the angular power density of the GWB. We also consider a rapid, frequentist alternative based on information criteria statistics. We verify our methods with a series of injection-and-recovery simulations, finding that the standard counting argument would lead to drastic overfitting of the data and that our data-driven methods reduce the number of parameters in the models by one to three orders of magnitude yet can achieve higher resolution than the standard counting argument when justified by the data. We make our sampler and frequentist method implementation available on GitHub.
