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The DESI Legacy Survey is a digital sky survey with a large footprint compared to other Earth-based surveys, covering both the Northern and Southern hemispheres. This paper shows the distribution of the spin directions of spiral galaxies imaged by DESI Legacy Survey. A

simple analysis of dividing nearly 1.3 × 106 spiral galaxies into two hemispheres shows a higher number of galaxies spinning counterclockwise in the Northern hemisphere, and a higher number of galaxies spinning clockwise in the Southern hemisphere. That distribution is consistent with previous observations, but uses a far larger number of galaxies and a larger footprint. The larger footprint allows a comprehensive analysis without the need to fit the distribution into an a priori model, making this study different from all previous analyses of this kind. Fitting the spin directions of the galaxies to cosine dependence shows a dipole axis alignment with probability of P < 10−5. The analysis is done with a trivial selection of the galaxies, as well as simple explainable annotation algorithm that does not make use of any form of machine learning, deep learning, or pattern recognition. While further work will be required, these results are aligned with previous studies suggesting themore »possibility of a large-scale alignment of galaxy angular momentum.

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The nature of galaxy spin is still not fully known. Iye, Yagi, and Fukumoto (2021, AJ, 907, 123) applied a 3D analysis to a dataset of bright SDSS galaxies that was used in the past for photometric analysis. They showed that the distribution of spin directions of spiral galaxies is random, providing a dipole axis with low statistical significance of 0.29σ. However, to show random distribution, two decisions were made, each of which can lead to random distribution regardless of the real distribution of the spin direction of galaxies. The first decision was to limit the dataset arbitrarily to z < 0.1, which is a redshift range in which previous literature already showed that random distribution is expected. More importantly, while the 3D analysis requires the redshift of each galaxy, the analysis was done with the photometric redshift. If the asymmetry existed, its signal is expected to be an order of magnitude weaker than the error of the photometric redshift, and therefore a low statistical signal under these conditions is expected. When using the exact same data without limiting to zphot < 0.1 and without using the photometric redshift, the distribution of the spin directions in that dataset showsmore »a statistical signal of >2σ. Code and data for reproducing the analysis are publicly available. These results are in agreement with other experiments with SDSS, Pan-STARRS, HST, and the DESI Legacy Survey. The paper also examines other previous studies that showed random distribution in galaxy spin directions. While further research will be required, the current evidence suggests that large-scale asymmetry between the number of clockwise and counterclockwise galaxies cannot be ruled out.

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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 aremore »aligned with previous observations of differences between galaxies based on their spin directions.« less

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 analyzesmore »the design of previous work that showed different results. Further research will be required to understand and validate these preliminary observations.« less

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.

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 when the error is randomly distributed between clockwise and counterclockwise galaxies. However, when the error is not random, even a small bias of 1% leads to a statistically significant cosine dependence that peaks at the celestial pole. Experiments with artificial datasets in which the distribution was not random showed strong cosine dependence even when the data did not form a full dipole axis alignment. Themore »analysis when using the unmodified data shows asymmetry profile similar to the profile shown in multiple previous studies using several different telescopes.« less

ABSTRACT Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical data bases in the information era, it can be assumed that very many outlier galaxies are included in existing and future astronomical data bases. However, manual search for these objects is impractical due to the required labour, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the data set by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.