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arXiv 2101.04068astro-ph.CO
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旋涡星系群中自旋方向非随机模式的排列分析

Analysis of the alignment of non-random patterns of spin directions in populations of spiral galaxies

Lior Shamir

摘要:

对具有相反自旋方向的星系的非随机分布的观测最近引起了相当大的关注。在这里,一种方法,用于识别余弦依赖性的星系的数据集注释其自旋方向的光,可以影响数据的统计分析的不同方面进行了描述。这些方面包括数据集中存在重复的对象,星系注释过程中的错误,以及不对称的非随机分布,不一定形成偶极或四极轴。结果表明,数据集中的重复对象可以人为地增加在数据中检测到的余弦依赖的可能性,但是需要非常高数量的重复对象才能导致轴的错误检测。当误差随机分布在顺时针和逆时针星系之间时,星系注释的不准确性对余弦依赖性的识别影响相对较小。然而,当误差不是随机的时,即使是1%的小偏差也会导致在天极达到峰值的统计上显著的余弦依赖性。人工数据集的分布不是随机的实验显示出很强的余弦依赖性,即使数据没有形成一个完整的偶极子轴对齐。当使用未修改的数据时,分析显示不对称轮廓与使用几个不同望远镜的多个先前研究中显示的轮廓相似。

英文摘要:

Observations of non-random distribution of galaxies with opposite spin directions have recently attracted considerable attention. Here, a method for identifying cosine-dependence in a dataset of galaxies annotated by their spin directions is described in the light of different aspects that can impact the statistical analysis of the data. These aspects include the presence of duplicate objects in a dataset, errors in the galaxy annotation process, and non-random distribution of the asymmetry that does not necessarily form a dipole or quadrupole axes. The results show that duplicate objects in the dataset can artificially increase the likelihood of cosine dependence detected in the data, but a very high number of duplicate objects is required to lead to a false detection of an axis. Inaccuracy in galaxy annotations has relatively minor impact on the identification of cosine dependence when the error is randomly distributed between clockwise and counterclockwise galaxies. However, when the error is not random, even a small bias of 1% leads to a statistically significant cosine dependence that peaks at the celestial pole. Experiments with artificial datasets in which the distribution was not random showed strong cosine dependence even when the data did not form a full dipole axis alignment. The analysis when using the unmodified data shows asymmetry profile similar to the profile shown in multiple previous studies using several different telescopes.

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。

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