论文标题
通过脉冲星定时阵列的随机重力波背景的准确表征，可能会重新升级
Accurate characterization of the stochastic gravitational-wave background with pulsar timing arrays by likelihood reweighting
论文作者
论文摘要
Nanohertz引力波的各向同性随机背景在Pulsar-Timing-Array数据集中产生过多的残余功率，并具有由Hellings-Downs函数描述的特征性脉冲间相关性。这些相关性显示为噪声协方差矩阵中的非对角术语，必须倒置以获得脉冲星阵阵列的可能性。因此，搜索需要进行许多可能评估的随机背景在计算上非常昂贵。我们提出了一种更有效的方法：我们首先通过忽略跨相关性来计算近似后代，然后通过重要性采样将其重新持续到确切的后验。我们表明，这项技术会导致准确的后代和边际似然比，因为近似和精确的后载相似，这使得重量重量特别准确。通过我们的方法可靠地估计，精确模型和近似模型的边际可能性之间的贝叶斯比率也可靠地估计至少为$ 10^6 $。
An isotropic stochastic background of nanohertz gravitational waves creates excess residual power in pulsar-timing-array datasets, with characteristic inter-pulsar correlations described by the Hellings-Downs function. These correlations appear as nondiagonal terms in the noise covariance matrix, which must be inverted to obtain the pulsar-timing-array likelihood. Searches for the stochastic background, which require many likelihood evaluations, are therefore quite computationally expensive. We propose a more efficient method: we first compute approximate posteriors by ignoring cross correlations, and then reweight them to exact posteriors via importance sampling. We show that this technique results in accurate posteriors and marginal likelihood ratios, because the approximate and exact posteriors are similar, which makes reweighting especially accurate. The Bayes ratio between the marginal likelihoods of the exact and approximate models, commonly used as a detection statistic, is also estimated reliably by our method, up to ratios of at least $10^6$.
