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  1. 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
    Free, publicly-accessible full text available April 1, 2023
  2. Frey, Sandor (Ed.)
    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
    Free, publicly-accessible full text available April 1, 2023
  3. 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.
    Free, publicly-accessible full text available January 1, 2023
  4. Free, publicly-accessible full text available January 1, 2023
  5. Free, publicly-accessible full text available January 1, 2023
  6. 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
  7. 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 datasetmore »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.

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  8. Galaxy images of the order of multi-PB are collected as part of modern digital sky surveys using robotic telescopes. While there is a plethora of imaging data available, the majority of the images that are captured resemble galaxies that are “regular”, i.e., galaxy types that are already known and probed. However, “novelty" galaxy types, i.e., little-known galaxy types are encountered on occasion. The astronomy community shows paramount interest in the novelty galaxy types since they contain the potential for scientific discovery. However, since these galaxies are rare, the identification of such novelty galaxies is not trivial and requires automation techniques. Since these novelty galaxies are by definition, not known, supervised 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 uses a large set of image features weighted by their entropy. To handle the impact of self-similar novelty galaxies, the most similar galaxies are ranked-ordered. In addition, Bag of Visual Words (BOVW) is assimilated to the problem of detecting novelty galaxies. Each image in the dataset is represented as a set of features made up of key-points and descriptors. Amore »histogram of the features is constructed and is leveraged to identify the neighbors of each of the images. Experimental results using data from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) show that the performance of the methods in detecting novelty galaxies is superior to 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.« less
  9. 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.
  10. In the past decade, deep neural networks, and specifically convolutional neural networks (CNNs), have been becoming a primary tool in the field of biomedical image analysis, and are used intensively in other fields such as object or face recognition. CNNs have a clear advantage in their ability to provide superior performance, yet without the requirement to fully understand the image elements that reflect the biomedical problem at hand, and without designing specific algorithms for that task. The availability of easy-to-use libraries and their non-parametric nature make CNN the most common solution to problems that require automatic biomedical image analysis. But while CNNs have many advantages, they also have certain downsides. The features determined by CNNs are complex and unintuitive, and therefore CNNs often work as a “Black Box”. Additionally, CNNs learn from any piece of information in the pixel data that can provide a discriminative signal, making it more difficult to control what the CNN actually learns. Here we follow common practices to test whether CNNs can classify biomedical image datasets, but instead of using the entire image we use merely parts of the images that do not have biomedical content. The experiments show that CNNs can provide high classificationmore »accuracy even when they are trained with datasets that do not contain any biomedical information, or can be systematically biased by irrelevant information in the image data. The presence of such consistent irrelevant data is difficult to identify, and can therefore lead to biased experimental results. Possible solutions to this downside of CNNs can be control experiments, as well as other protective practices to validate the results and avoid biased conclusions based on CNN-generated annotations.« less