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

TL;DR
This paper presents a method to analyze the non-random alignment of galaxy spin directions, examining factors like data duplicates and annotation errors, and finds that biases can significantly influence detected patterns.
Contribution
The study introduces a comprehensive approach to identify cosine dependence in galaxy spin data, accounting for various data quality issues and non-dipole asymmetries, advancing analysis techniques in this field.
Findings
Duplicate objects can artificially increase cosine dependence detection.
Small annotation biases can lead to significant false positives.
Non-random distributions can produce strong cosine dependence signals.
Abstract
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…
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