Patterns of galaxy spin directions in SDSS and Pan-STARRS show parity violation and multipoles

TL;DR

This study analyzes galaxy spin directions in SDSS and Pan-STARRS, revealing statistically significant parity violations and multipole alignments that vary with redshift and observation direction, suggesting potential cosmological asymmetries.

Contribution

It provides the first large-scale, automated analysis showing significant parity violation in galaxy spins across two surveys, indicating possible cosmological anisotropies.

Findings

Significant asymmetry in galaxy spin directions in SDSS data.

Redshift dependence shows increasing asymmetry at higher redshifts.

Consistent multipole alignments found in both SDSS and Pan-STARRS data.

Abstract

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$\sigma$, which grows to >8$\sigma$ 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.