Analysis of the Alignment of Non-Random Patterns of Spin Directions in Populations of Spiral Galaxies

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