arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1209.6428gr-qcastro-ph.IM
收藏

应用贝叶斯推断对第一次国际脉冲星定时阵列数据挑战

Applying Bayesian Inference to the first International Pulsar Timing Array data challenge

Neil J. Cornish

摘要:

这是我用来分析IPTA挑战1数据集的技术的一个非常简短的总结。我尝试了很多方法,失败的比成功的多,但最终我发现了两种方法,它们似乎是基于使用开放数据集进行的测试。一种方法直接使用时域数据,另一种方法使用数据的专门构造的傅立叶变换。通过TEMPO 2运行原始数据,以产生用于分析的减少的定时残差。使用标准马尔可夫链蒙特卡罗技术从模型参数的后验分布函数产生样本。模型参数包括阵列中每个脉冲星的引力波振幅和谱斜率,以及白噪声振幅。虽然红色时序噪声仅包含在数据集3中,但我发现有必要在所有分析中包含有效的红色噪声,以解释TEMPO 2时序拟合引入的一些杂散效应。这为每个脉冲星增加了一个额外的振幅和斜率参数,所以我的36个脉冲星残差的整体模型有110个参数。作为使用有效红噪声模型的替代方案,我还尝试在寻找引力波信号的同时,同时重新拟合时序模型模型,但由于目前尚不清楚的原因,这种方法并不十分成功。我简单地评论一下可以改进算法的方法。我对三个封闭(盲)数据集中引力波振幅的最佳估计是:(1)$A=(7.3\pm 1.0)\times 10^{-15}$ ;(2)$A=(5.7\pm 0.6)\times 10^{-14}$ ;(3)$A=(4.6\pm 1.3)\times 10^{-15}$。

英文摘要:

This is a very brief summary of the techniques I used to analyze the IPTA challenge 1 data sets. I tried many things, and more failed than succeeded, but in the end I found two approaches that appear to work based on tests done using the open data sets. One approach works directly with the time domain data, and the other works with a specially constructed Fourier transform of the data. The raw data was run through TEMPO2 to produce reduced timing residuals for the analysis. Standard Markov Chain Monte Carlo techniques were used to produce samples from the posterior distribution function for the model parameters. The model parameters include the gravitational wave amplitude and spectral slope, and the white noise amplitude for each pulsar in the array. While red timing noise was only included in Dataset 3, I found that it was necessary to include effective red noise in all the analyses to account for some of the spurious effects introduced by the TEMPO2 timing fit. This added an additional amplitude and slope parameter for each pulsar, so my overall model for the 36 pulsars residuals has 110 parameters. As an alternative to using an effective red noise model, I also tried to simultaneously re-fit the timing model model while looking for the gravitational wave signal, but for reasons that are not yet clear, this approach was not very successful. I comment briefly on ways in which the algorithms could be improved. My best estimates for the gravitational wave amplitudes in the three closed (blind) data sets are: (1) $A=(7.3\pm 1.0)\times 10^{-15}$; (2) $A=(5.7\pm 0.6)\times 10^{-14}$; and (3) $A=(4.6\pm 1.3)\times 10^{-15}$.

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。

↑