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The ability of digital sky surveys to collect and store very large amounts of data provides completely new ways to study the local universe. Perhaps one of the most provocative observations reported with such tools is the asymmetry between galaxies with clockwise and counterclockwise spin patterns. Here, I use∼1.7 × 10⁵spiral galaxies from Sloan Digital Sky Survey (SDSS) and sort them by their spin patterns (clockwise or counterclockwise) to identify and profile a possible large‐scale pattern of the distribution of galaxy spin patterns as observed from Earth. The analysis shows asymmetry between the number of clockwise and counterclockwise spiral galaxies imaged by SDSS and a dipole axis. These findings largely agree with previous reports using smaller datasets. The probability of the differences between the number of galaxies occurring by chance isp< 4 × 10⁻⁹, and the probability of an asymmetry axis occurring by mere chance isp< 1.4×10⁻⁵.

 

The distribution of the spin directions of spiral galaxies in the Sloan Digital Sky Survey has been a topic of debate in the past two decades, with conflicting conclusions reported even in cases where the same data were used. Here, we follow one of the previous experiments by applying the SpArcFiRe algorithm to annotate the spin directions in an original dataset of Galaxy Zoo 1. The annotation of the galaxy spin directions is carried out after the first step of selecting the spiral galaxies in three different manners: manual analysis by Galaxy Zoo classifications, by a model-driven computer analysis, and with no selection of spiral galaxies. The results show that when spiral galaxies are selected by Galaxy Zoo volunteers, the distribution of their spin directions as determined by SpArcFiRe is not random, which agrees with previous reports. When selecting the spiral galaxies using a model-driven computer analysis or without selecting the spiral galaxies at all, the distribution is also not random. Simple binomial distribution analysis shows that the probability of the parity violation to occur by chance is lower than 0.01. Fitting the spin directions as observed from the Earth to cosine dependence exhibits a dipole axis with statistical strength of 2.33 σ to 3.97 σ . These experiments show that regardless of the selection mechanism and the analysis method, all experiments show similar conclusions. These results are aligned with previous reports using other methods and telescopes, suggesting that the spin directions of spiral galaxies as observed from the Earth exhibit a dipole axis formed by their spin directions. Possible explanations can be related to the large-scale structure of the universe or to the internal structure of galaxies. The catalogs of annotated galaxies generated as part of this study are available. 

The ability to collect unprecedented amounts of astronomical data has enabled the nomical data has enabled the stu scientific questions that were impractical to study in the pre-information era. This study uses large datasets collected by four different robotic telescopes to profile the large-scale distribution of the spin directions of spiral galaxies. These datasets cover the Northern and Southern hemispheres, in addition to data acquired from space by the Hubble Space Telescope. The data were annotated automatically by a fully symmetric algorithm, as well as manually through a long labor-intensive process, leading to a dataset of nearly 10^6 galaxies. The data show possible patterns of asymmetric distribution of the spin directions, and the patterns agree between the different telescopes. The profiles also agree when using automatic or manual annotation of the galaxies, showing very similar large-scale patterns. Combining all data from all telescopes allows the most comprehensive analysis of its kind to date in terms of both the number of galaxies and the footprint size. The results show a statistically significant profile that is consistent across all telescopes. The instruments used in this study are DECam, HST, SDSS, and Pan-STARRS. The paper also discusses possible sources of bias and analyzes the design of previous work that showed different results. Further research will be required to understand and validate these preliminary observations. 

Spiral galaxies can spin clockwise or counterclockwise, and the spin direction of a spiral galaxy is a clear visual characteristic. Since in a sufficiently large universe the Universe is expected to be symmetric, the spin direction of a galaxy is merely the perception of the observer, and therefore, galaxies that spin clockwise are expected to have the same characteristics of galaxies spinning counterclockwise. Here, machine learning is applied to study the possible morphological differences between galaxies that spin in opposite directions. The dataset used in this study is a dataset of 77,840 spiral galaxies classified by their spin direction, as well as a smaller dataset of galaxies classified manually. A machine learning algorithm was applied to classify between images of clockwise galaxies and counterclockwise galaxies. The results show that the classifier was able to predict the spin direction of the galaxy by its image in accuracy higher than mere chance, even when the images in one of the classes were mirrored to create a dataset with consistent spin directions. That suggests that galaxies that seem to spin clockwise to an Earth-based observer are not necessarily fully symmetric to galaxies that spin counterclockwise; while further research is required, these results are aligned with previous observations of differences between galaxies based on their spin directions. 

In the past several decades, multiple cosmological theories that are based on the contention that the Universe has a major axis have been proposed. Such theories can be based on the geometry of the Universe, or multiverse theories such as black hole cosmology. The contention of a cosmological-scale axis is supported by certain evidence such as the dipole axis formed by the CMB distribution. Here I study another form of the cosmological-scale axis, based on the distribution of the spin direction of spiral galaxies. Data from four different telescopes are analyzed, showing nearly identical axis profiles when the distribution of the redshifts of the galaxies is similar. 

Modern digital sky surveys utilize robotic telescopes that collect extremely large multi- PB astronomical databases. While these databases can contain billions of galaxies, most of the galaxies are “regular” galaxies of known galaxy types. However, a small portion of the galaxies is rare “peculiar” galaxies that are not yet known. These unknown galaxies are of paramount scientific interest, but due to the enormous size of astronomical databases they are practically impossible to find without automation. Since these novelty galaxies are, by definition, not known, machine learning models cannot be trained to detect them. In this paper, an unsupervised machine learning method for automatic detection of novelty galaxies in large databases is proposed. The method is based on a large and comprehensive set of numerical image content descriptors weighted by their entropy, and the farthest neighbors are ranked-ordered to handle self-similar peculiar galaxies that are expected in the very large datasets. Experimental results using data from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) show that the ability of the method to detect novelty galaxies outperforms other shallow learning methods such as one-class SVM, Local Outlier Factor, and K-Means, and also newer deep learning-based methods such as auto-encoders. The dataset used to evaluate the method is publicly available and can be used as a benchmark to test future algorithms for automatic detection of peculiar galaxies.