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arXiv 2406.16331gr-qcastro-ph.HE
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脉冲星计时阵列数据中连续引力波的高效贝叶斯推断与模型选择

Efficient Bayesian inference and model selection for continuous gravitational waves in pulsar timing array data

Bence Bécsy

AI总结:

提出结合矩阵运算预计算插值、引力波相位半解析边缘化和距离不确定性数值边缘化的高效贝叶斯方法,将 NANOGrav 15yr 连续引力波分析由数周缩短至分钟级,并支持模型选择与大规模模拟。

摘要:

寻找并表征来自单个超大质量黑洞双星的引力波是脉冲星计时阵列实验的核心目标,这将需要能够在快速增长的数据集上保持高效的分析方法。本文提出一种建立在三个关键要素上的新方法:i)预计算并插值昂贵的矩阵运算;ii)对脉冲星处的引力波相位进行半解析边缘化;iii)对脉冲星距离不确定性进行数值边缘化。借助这些改进,在约 O(1 小时) 的准备阶段之后,只需数分钟即可分析近期的 NANOGrav 15yr 数据集,而以往方法需要数天至数周。同一准备结果可用于在任意正弦确定性模型下高效分析该数据集。特别是,这将通过支持对竞争模型(例如非相干、单极或偶极正弦波模型)以及用于虚警研究的置乱数据集进行高效分析,助力检验双星假说。对于新的数据实现,同一准备结果可在数分钟内更新,从而支持大规模模拟研究。

英文摘要:

Finding and characterizing gravitational waves from individual supermassive black hole binaries is a central goal of pulsar timing array experiments, which will require analysis methods that can be efficient on our rapidly growing datasets. Here we present a novel approach built on three key elements: i) precalculating and interpolating expensive matrix operations; ii) semi-analytically marginalizing over the gravitational-wave phase at the pulsars; iii) numerically marginalizing over the pulsar distance uncertainties. With these improvements the recent NANOGrav 15yr dataset can be analyzed in minutes after an $\mathcal{O}(1\ \mathrm{hour})$ setup phase, instead of an analysis taking days-weeks with previous methods. The same setup can be used to efficiently analyze the dataset under any sinusoidal deterministic model. In particular, this will aid testing the binary hypothesis by allowing for efficient analysis of competing models (e.g. incoherent, monopolar, or dipolar sine wave model) and scrambled datasets for false alarm studies. The same setup can be updated in minutes for new realizations of the data, which enables large simulation studies.

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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