arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0905.2854astro-ph.CO
收藏

五年WMAP数据中的非高斯特征用各向同性标度指数识别

Non-Gaussian Signatures in the five-year WMAP data as identified with isotropic scaling indices

G. Rossmanith, C. Raeth, A. J. Banday, G. Morfill

摘要:

我们继续通过标度指数法(SIM,Raeth,Schuecker & Banday 2007)对CMB的非高斯性进行分析,并将该方法应用于WMAP的5年数据。我们将每个结果与1000个蒙特卡罗模拟进行比较,模拟最佳拟合$ΛCDM$模型的高斯特性。基于标度指数,计算了与标度相关的经验概率分布、这些分布的矩以及它们的$χ^2$组合,获得了与3年数据的先前分析相似的结果:我们得出的证据,非高斯的概率高达97.3%的平均值时,关于KQ 75-掩蔽的全天空,并通过对角$χ^2$统计在所有考虑的长度尺度上求和。仅看北半球或南半球,我们分别获得高达98.5%或96.6%。对于标准偏差,这些结果对于全天空显示为95.6%(99.7%北,89.4%南),对于$χ^2$ -两种测量的组合显示为97.4%(99.1%北,95.5%南)。通过对旋转半球的分析,我们发现数据中存在明显的不对称性。除了这些调查,我们提出了一种方法,填充高斯噪声的掩模,以消除边界效应所造成的掩模。在这种技术的帮助下,我们确定了地图上的几个局部特征,其中最重要的一个是众所周知的冷点。当从分析中排除所有这些点时,与高斯性的偏差增加,这表明所发现的局部异常不是全局检测到非高斯性的原因,而是实际上平均抑制了偏差。然而,我们对每个波段和每年的分析表明,检测到的异常是由于前景效应造成的可能性很小。

英文摘要:

We continue the analysis of non-Gaussianities in the CMB by means of the scaling index method (SIM, Raeth, Schuecker & Banday 2007) by applying this method on the 5-year WMAP data. We compare each of the results with 1000 Monte Carlo simulations mimicing the Gaussian properties of the best fit $ΛCDM$-model. Based on the scaling indices, scale-dependent empirical probability distributions, moments of these distributions and $χ^2$-combinations of them are calculated, obtaining similar results as in the former analysis of the 3-year data: We derive evidence for non-Gaussianity with a probability of up to 97.3% for the mean when regarding the KQ75-masked full sky and summing up over all considered length scales by means of a diagonal $χ^2$-statistics. Looking at only the northern or southern hemisphere, we obtain up to 98.5% or 96.6%, respectively. For the standard deviation, these results appear as 95.6% for the full sky (99.7% north, 89.4% south) and for a $χ^2$-combination of both measurements as 97.4% (99.1% north, 95.5% south). By performing an analysis of rotated hemispheres, we detect an obvious asymmetry in the data. In addition to these investigations, we present a method of filling the mask with Gaussian noise to eliminate boundary effects caused by the mask. With the help of this technique, we identify several local features on the map, of which the most significant one turns out to be the well-known cold spot. When excluding all these spots from the analysis, the deviation from Gaussianity increases, which shows that the discovered local anomalies are not the reason of the global detection of non-Gaussianity, but actually were damping the deviations on average. Our analyses per band and per year suggest, however, that it is very unlikely that the detected anomalies are due to foreground effects.

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。

↑