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With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
A concise and up-to-date guide to the shape of galaxies and how they can be classified, by one of the pioneers of the field.
A thought provoking study of the powerful impact of images in guiding astronomers' understanding of galaxies through time.
No modern astronomer made a more profound contribution to our understanding of the cosmos than did Edwin Hubble, who first conclusively demonstrated that the universe is expanding. Basing his theory on the observation of the change in distanct galaxies, called red shift, Hubble showed that this is a Doppler effect, or alteration in the wavelength of light, resulting from the rapid motion of celestial objects away from Earth. In 1935, Hubble described his principal observations and conclusions in the Silliman lectures at Yale University. These lectures were published the following year as "The Realm of the Nebulae," which quickly became a classic work.
The morphological scheme devised by Hubble and followers to classify galaxies has proven over many decades to be quite effective in directing our quest for the fundamental pa rameters describing the extragalactic manifold. This statement is however far more true for spirals than for ellipticals. Echoing the concluding remarks in Scott Tremaine's sum mary talk at the Princeton meeting on Structure and Dynamics of Elliptical Galaxies, "the Hubble classification of spirals is useful because many properties of spirals (gas con tent, spiral arm morphology, bulge prominence, etc. ) all correlate with Hubble time. By contrast, almost nothing correlates with the elliptical Hubble sequence El to E7. " During the last few years much effort has been put into the search for a more meaningful classification of ellipticals than Hubble's. Concomitantly, forwarded by some provocative conjectures by R. Michard, the classical question of whether E galaxies form a physically homogeneous family has been brushed up once more. Results of these and other parallel studies look rather promising and point to suture part of the dichotomy between ellipticals and disk galaxies which had become popular in the early eighties, owing to dynamical arguments. At the same time it appears more and more clear that, besides the usual genetic varieties of galaxies, products of environmental evolution must also be contemplated in building our modern picture of the "reign of galaxies" . The above considerations prompted us to solicit Prof.
Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.
Galaxies, along with their underlying dark matter halos, constitute the building blocks of structure in the Universe. Of all fundamental forces, gravity is the dominant one that drives the evolution of structures from small density seeds at early times to the galaxies we see today. The interactions among myriads of stars, or dark matter particles, in a gravitating structure produce a system with fascinating connotations to thermodynamics, with some analogies and some fundamental differences. Ignacio Ferreras presents a concise introduction to extragalactic astrophysics, with emphasis on stellar dynamics, and the growth of density fluctuations in an expanding Universe. Additional chapters are devoted to smaller systems (stellar clusters) and larger ones (galaxy clusters). Fundamentals of Galaxy Dynamics, Formation and Evolution is written for advanced undergraduates and beginning postgraduate students, providing a useful tool to get up to speed in a starting research career. Some of the derivations for the most important results are presented in detail to enable students appreciate the beauty of maths as a tool to understand the workings of galaxies. Each chapter includes a set of problems to help the student advance with the material.
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines