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arXiv 1312.3293astro-ph.CO
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用组合估计寻找Planck CMB映射的原始非高斯性

Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator

C. P. Novaes, A. Bernui, I. S. Ferreira, C. A. Wuensche

摘要:

宇宙微波背景辐射(CMB)数据中包含了宇宙非常早期的信息,因此,对CMB数据中高斯性偏差的广泛研究非常重要。最近的分析从普朗克CMB数据不排除存在的非高斯的小振幅,虽然他们是符合高斯假设。使用不同的技术是必不可少的,以提供信息的类型和振幅的非高斯CMB数据。特别是,我们发现有趣的是,构建一个估计的基础上,两个强大的统计工具,似乎是足够敏感的检测微小的偏差,从高斯CMB地图的组合。该估计器将Minkowski泛函与神经网络相结合,最大化了广泛用于研究非高斯信号的工具,并加强了另一种用于识别数据集中模式的工具。我们测试我们的估计通过分析模拟CMB地图污染与不同数量的本地原始非高斯量化的无量纲参数fNL。我们将其应用于这些CMB地图集,并发现\gtrsim 98%的阳性检测机会,即使对于小强度局部非高斯性,如fNL = 38 +/- 18,普朗克数据的大角度尺度的当前限制。此外,我们测试了区分初级和次级非高斯性的适用性,并发现我们的方法成功地对约95%的测试地图进行了分类。此外,我们分析了前景清洁普朗克地图获得非高斯在大角度的约束,在最近的约束很好的协议。最后,我们还测试了我们的估计器的鲁棒性,包括切割天空面具和现实的噪声地图测量普朗克,获得成功的结果。

英文摘要:

The extensive search for deviations from Gaussianity in cosmic microwave background radiation (CMB) data is very important due to the information about the very early moments of the universe encoded there. Recent analyses from Planck CMB data do not exclude the presence of non-Gaussianity of small amplitude, although they are consistent with the Gaussian hypothesis. The use of different techniques is essential to provide information about types and amplitudes of non-Gaussianities in the CMB data. In particular, we find interesting to construct an estimator based upon the combination of two powerful statistical tools that appears to be sensitive enough to detect tiny deviations from Gaussianity in CMB maps. This estimator combines the Minkowski functionals with a Neural Network, maximizing a tool widely used to study non-Gaussian signals with a reinforcement of another tool designed to identify patterns in a data set. We test our estimator by analyzing simulated CMB maps contaminated with different amounts of local primordial non-Gaussianity quantified by the dimensionless parameter fNL. We apply it to these sets of CMB maps and find \gtrsim 98% of chance of positive detection, even for small intensity local non-Gaussianity like fNL = 38 +/- 18, the current limit from Planck data for large angular scales. Additionally, we test the suitability to distinguish between primary and secondary non-Gaussianities and find out that our method successfully classifies ~ 95% of the tested maps. Furthermore, we analyze the foreground-cleaned Planck maps obtaining constraints for non-Gaussianity at large-angles that are in good agreement with recent constraints. Finally, we also test the robustness of our estimator including cut-sky masks and realistic noise maps measured by Planck, obtaining successful results as well.

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