arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1409.3876astro-ph.CO
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基于神经网络的普朗克CMB映射原始非高斯性估计

A Neural-Network based estimator to search for primordial non-Gaussianity in Planck CMB maps

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

摘要:

我们提出了一个升级的组合估计,Minkowski泛函和神经网络的基础上,具有优异的性能,在检测原始的非高斯模拟地图,也包含一个加权的混合物的银河污染,除了真正的像素的噪声从普朗克宇宙微波背景辐射数据。我们严格测试了我们的估计器的效率,考虑到前景清洁普朗克地图中残余非高斯性的几种合理情况,直觉地优化神经网络的训练过程,以区分具有原始和二次非高斯特征的污染。我们寻找约束的原始本地非高斯在大角度尺度上的前景清洁普朗克地图。对于$\mathtt{SMICA}$地图,我们发现${f}_{\rm \,NL} = 33 \pm 23$,在$1σ$置信水平,在极好的协议与WMAP-9年和普朗克的结果。此外,对于其他三个普朗克地图,我们得到了类似的限制与区间${f}_{\rm \,NL} \in [33, 41]$的值,伴随着事实,这些地图表现出不同的功能,在报告的分析,如具有不同的像素的噪声强度。

英文摘要:

We present an upgraded combined estimator, based on Minkowski Functionals and Neural Networks, with excellent performance in detecting primordial non-Gaussianity in simulated maps that also contain a weighted mixture of Galactic contaminations, besides real pixel's noise from Planck cosmic microwave background radiation data. We rigorously test the efficiency of our estimator considering several plausible scenarios for residual non-Gaussianities in the foreground-cleaned Planck maps, with the intuition to optimize the training procedure of the Neural Network to discriminate between contaminations with primordial and secondary non-Gaussian signatures. We look for constraints of primordial local non-Gaussianity at large angular scales in the foreground-cleaned Planck maps. For the $\mathtt{SMICA}$ map we found ${f}_{\rm \,NL} = 33 \pm 23$, at $1σ$ confidence level, in excellent agreement with the WMAP-9yr and Planck results. In addition, for the other three Planck maps we obtain similar constraints with values in the interval ${f}_{\rm \,NL} \in [33, 41]$, concomitant with the fact that these maps manifest distinct features in reported analyses, like having different pixel's noise intensities.

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