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arXiv 2305.04464astro-ph.IMastro-ph.HEgr-qc
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迈向纳赫兹引力波稳健检测

Toward robust detections of nanohertz gravitational waves

Monash University(莫纳什大学);OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery(ARC引力波发现卓越中心);CSIRO, Space and Astronomy(CSIRO空间与天文部);Swinburne University of Technology(斯威本科技大学)

Valentina Di Marco, Andrew Zic, Matthew T. Miles, Daniel J. Reardon, Eric Thrane, Ryan M. Shannon

AI总结:

研究探讨了脉冲星计时阵列在检测纳赫兹引力波背景时面临的挑战,分析了天空 scrambling 和相位 scrambling 的饱和问题,并提出增加独立 scrambling 数量和重新框架背景估计方法的解决方案。

摘要:

最近在脉冲星计时阵列中观测到共同红噪声过程,表明纳赫兹引力波检测可能即将到来。然而,要确认该红噪声来自引力波,必须观测到Hellings-Downs曲线。这受到脉冲星噪声复杂建模的阻碍。不恰当的噪声模型可能导致假阳性检测。使用bootstrap方法如天空 scrambling 和相位 shifts 来估计背景是关键。本文研究了当前PTA实验使用

英文摘要:

The recent observation of a common red-noise process in pulsar timing arrays (PTAs) suggests that the detection of nanohertz gravitational waves might be around the corner. However, in order to confidently attribute this red process to gravitational waves, one must observe the Hellings-Downs curve -- the telltale angular correlation function associated with a gravitational-wave background. This effort is complicated by the complex modelling of pulsar noise. Without proper care, mis-specified noise models can lead to false-positive detections. Background estimation using bootstrap methods such as sky scrambles and phase shifts, which use the data to characterize the noise, are therefore important tools for assessing significance. We investigate the ability of current PTA experiments to estimate their background with "quasi-independent" scrambles -- characterized by a statistical "match" below the fiducial value: $|M|<0.1$. We show that sky scrambling is affected by "saturation" after $O(10)$ quasi-independent realizations; subsequent scrambles are no longer quasi-independent. We show phase scrambling saturates after $O(100)$ quasi-independent realizations. With so few independent scrambles, it is difficult to make reliable statements about the $\gtrsim 5 σ$ tail of the null distribution of the detection statistic. We discuss various methods by which one may increase the number of independent scrambles. We also consider an alternative approach wherein one re-frames the background estimation problem so that the significance is calculated using statistically dependent scrambles. The resulting $p$-value is in principle well-defined but may be susceptible to failure if assumptions about the data are incorrect.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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