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Searching cosmic strings network in the CMB

机译:在CMB中搜索Cosmic Strings网络

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摘要

The CMB stochastic field provides us with a unique opportunity to search for the predicted imprints of various cosmological theories. In this work, we explore the detectability of the cosmic string (CS) network traces in the pixel-space through its gravitational impact on the CMB anisotropies, i.e. the Gott–Kaiser–Stebbins effect. First, in a classical approach, we use a series of multi-scale edge-detection algorithms followed by certain critical and excursion sets measures on the CMB simulated data (with different levels of contributions from the CSs). With this pipeline the minimum value of detectable CS’s tension for noiseless maps with a resolution of 0.9′ is found to be Gμ ? 4.3 × 10?10. Calling for the help the power of the machine learning algorithms with our proposed feature vectors, decreases the above lower bound to Gμ ? 2.1 × 10?10. The methods developed and used in this work are quite general and applicable to a wide variety of pattern search problems in the various stochastic fields ranging from cosmological random fields to other natural and artificial complex systems.
机译:CMB随机字段为我们提供了独特的机会,可以搜索各种宇宙理论的预测印记。在这项工作中,我们通过其对CMB各向异性的引力影响来探讨宇宙串(CS)网络迹线在像素空间中的可检测性,即GOTT-Kaiser-Stebbins效应。首先,在经典方法中,我们使用一系列多尺度边缘检测算法,然后是某些关键和偏移在CMB模拟数据上的措施(具有来自CSS的不同贡献)。通过该流水线,可检测到的CS张力的最小值,具有0.9'分辨率的无噪声地图的张力的张力的张力是Gμ? 4.3×10?10。呼唤帮助机器学习算法的功率与我们提出的特征向量,将上述下限降低到Gμ? 2.1×10?10。在这项工作中开发和使用的方法非常一般,并且适用于各种随机领域的各种模式搜索问题,范围从宇宙学随机字段到其他天然和人造复杂系统。

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