论文标题
SDS和Pan-Starrs中星系旋转方向的模式显示出平等的违规和多重
Patterns of galaxy spin directions in SDSS and Pan-STARRS show parity violation and multipoles
论文作者
论文摘要
检查了带有光谱的$ \ sim6.4 \ cdot10^4 $ sdss螺旋星系的分布，并将其与$ \ sim3.3 \ cdot10^4 $ pan-starrs星系的分布进行了比较。该分析表明，具有相反自旋方向的SDSS星系数量之间具有统计学意义的不对称性，以及不对称的大小和方向随观察方向和红移而变化。红移依赖性表明，随着红移的较高，SDSS星系的自旋方向的分布变得更不对称。将星系旋转方向的分布拟合到四极对齐的分布可提供统计显着性> 5 $σ$的适应性，当使用只有z> 0.15的星系时，它就会增长到> 8 $σ$。与Pan-Starrs星系的类似分析提供了几乎与SDSS星系分析相同的偶极子和四极比对，表明不对称的来源不一定是特定望远镜系统中的某些未知缺陷。尽管这些观察结果显然是挑衅的，但没有已知的错误可以以这种形式表现出来。数据分析过程是完全自动的，并使用具有定义规则的确定性和对称算法。它不涉及可以导致人类感知偏见的手动分析，也不涉及可以捕获人类偏见或其他细微差异的机器学习，而这些差异由于机器学习过程的复杂性而难以识别。同样，有望在星系注释过程中出现误差，在天空的所有部分都显示出一致的偏差，而不是随着观察方向而改变以形成清晰可定义的模式。
The distribution of spin directions of $\sim6.4\cdot10^4$ SDSS spiral galaxies with spectra was examined, and compared to the distribution of $\sim3.3\cdot10^4$ Pan-STARRS galaxies. The analysis shows a statistically significant asymmetry between the number of SDSS galaxies with opposite spin directions, and the magnitude and direction of the asymmetry changes with the direction of observation and with the redshift. The redshift dependence shows that the distribution of the spin direction of SDSS galaxies becomes more asymmetric as the redshift gets higher. Fitting the distribution of the galaxy spin directions to a quadrupole alignment provides fitness with statistical significance >5$σ$, which grows to >8$σ$ when just galaxies with z>0.15 are used. Similar analysis with Pan-STARRS galaxies provides dipole and quadrupole alignments nearly identical to the analysis of SDSS galaxies, showing that the source of the asymmetry is not necessarily a certain unknown flaw in a specific telescope system. While these observations are clearly provocative, there is no known error that could exhibit itself in such form. The data analysis process is fully automatic, and uses deterministic and symmetric algorithms with defined rules. It does not involve either manual analysis that can lead to human perceptual bias, or machine learning that can capture human biases or other subtle differences that are difficult to identify due to the complex nature of machine learning processes. Also, an error in the galaxy annotation process is expected to show consistent bias in all parts of the sky, rather than change with the direction of observation to form a clear and definable pattern.
