论文标题
寻求共形循环宇宙学的CMB签名
The quest for CMB signatures of Conformal Cyclic Cosmology
论文作者
论文摘要
宇宙微波背景（CMB）中低变化和鹰点的圆圈分别是由黑洞合并和黑洞蒸发在宇宙的先前循环中的，这可能是R. Penrose引入的保形循环宇宙学模型（CCC）的可能证据。我们向Planck和WMAP CMB数据中的这种低变异圈提出了高分辨率搜索，并介绍了基于RESNET18算法的机器学习开源软件Hawkingnet，以搜索CMB中的Hawking Points。我们发现，由一些异常明亮（高温）或黑暗（低温）像素组成的斑点错误地导致了许多低变化圈的区域，因此在以前的工作中应用了搜索标准时，近距离低相位圆圈的集合。 Gurzadyan，R。Penrose]。从数据中删除这些斑点后，找不到统计学上显着的低变化圈。关于霍金点，当使用高斯温度振幅模型在1度开头和考虑到异常亮度的斑点之后，也没有发现统计学上的显着证据。数据本身就是霍金点的残留物，这本身就是低变化和/或周围低温的圈子的支持。当前可用的CMB数据中缺乏此类统计学上很重要的特征并没有反驳CCC模型，但意味着需要更高的分辨率CMB数据和/或基于CCC的预测来进一步追求CCC签名的搜索
Circles of low-variance and Hawking points in the Cosmic Microwave Background (CMB), resulting from black hole mergers and black hole evaporation, respectively, in a previous cycle of the universe, have been predicted as possible evidence for the Conformal Cyclic Cosmology model (CCC) introduced by R. Penrose. We present a high-resolution search for such low-variance circles in the Planck and WMAP CMB data, and introduce HawkingNet, our machine learning open-source software based on a ResNet18 algorithm, to search for Hawking points in the CMB. We find that spots consisting of a few unusually bright (high-temperature) or dark (low-temperature) pixels, erroneously lead to regions with many low-variance circles, and consequently sets of near-concentric low-variance circles, when applying the search criteria used in previous work [V.G. Gurzadyan, R. Penrose]. After removing those spots from the data, no statistically significant low-variance circles can be found. Concerning Hawking points, also no statistically significant evidence is found when using a Gaussian temperature amplitude model over 1 degree opening angle and after accounting for spots of unusual brightness. That the unusual spots in the data are themselves remnants of Hawking points is not supported by low-variance and/or low-temperature circles around them. The absence of such statistically-significant distinct features in the currently available CMB data does not disprove the CCC model, but implies that higher resolution CMB data and/or refined CCC based predictions are needed to pursue the search for CCC signatures further
