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arXiv 2303.09615astro-ph.IMastro-ph.HE
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用于刻画脉冲星计时阵列中多个相关信号的广义最优统计量

Generalized optimal statistic for characterizing multiple correlated signals in pulsar timing arrays

Shashwat C. Sardesai, Sarah J. Vigeland, Kyle A. Gersbach, Stephen R. Taylor

AI总结:

本文提出多分量最优统计量(MCOS),通过同时拟合多个空间相关性,改进脉冲星计时阵列中对引力波背景等信号的检测准确性,降低错误空间相关性的误检风险,并能恢复多个相关信号。

摘要:

最优统计量(OS)是脉冲星计时阵列(PTA)数据中空间相关信号振幅和显著性的频率学派估计量,被广泛用于搜寻引力波背景(GWB)。然而,OS 无法完美区分不同的空间相关性。在本文中,我们引入了多分量最优统计量(MCOS):这是 OS 的一种推广,允许同时对数据拟合多个相关性。我们使用模拟数据表明,该方法能更准确地恢复注入的空间相关信号,特别是降低了以错误空间相关性误检出信号的风险。我们还证明,该方法可用于恢复多个相关信号。

英文摘要:

The optimal statistic (OS) is a frequentist estimator for the amplitude and significance of a spatially-correlated signal in pulsar timing array (PTA) data, and it is widely used to search for the gravitational wave background (GWB). However, the OS cannot perfectly distinguish between different spatial correlations. In this paper, we introduce the multiple component optimal statistic (MCOS): a generalization of the OS that allows for multiple correlations to be simultaneously fit to the data. We use simulated data to show that this method more accurately recovers injected spatially correlated signals, and in particular reduces the risk of a false detection of a signal with the wrong spatial correlation. We also demonstrate that this method can be used to recover multiple correlated signals.

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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