Discovery And Classification In Astronomy

This book shows that astronomical discovery is a complex and ongoing process comprising various stages of research, interpretation and understanding.

"Astronomical discovery involves more than detecting something previously unseen. The reclassification of Pluto as a dwarf planet in 2006, and the controversy it generated, shows that discovery is a complex and ongoing process - one comprising various stages of research, interpretation, and understanding. Ranging from Galileo's observation of Jupiter's satellites, Saturn's rings, and star clusters, to Herschel's nebulae and the modern discovery of quasars and pulsars, Steven J. Dick's comprehensive history identifies the concept of "extended discovery" as the engine of progress in astronomy. The text traces more than 400 years of telescopic observation, exploring how the signal discoveries of new astronomical objects relate to and inform one another, and why controversies such as Pluto's reclassification are commonplace in the field. The volume is complete with a detailed classification system for known classes of astronomical objects, offering students, researchers, and amateur observers a valuable reference and guide"--

Astronomical discovery involves more than detecting something previously unseen. The reclassification of Pluto as a dwarf planet in 2006, and the controversy it generated, shows that discovery is a complex and ongoing process – one comprising various stages of research, interpretation and understanding. Ranging from Galileo's observation of Jupiter's satellites, Saturn's rings and star clusters, to Herschel's nebulae and the modern discovery of quasars and pulsars, Steven J. Dick's comprehensive history identifies the concept of 'extended discovery' as the engine of progress in astronomy. The text traces more than 400 years of telescopic observation, exploring how the signal discoveries of new astronomical objects relate to and inform one another, and why controversies such as Pluto's reclassification are commonplace in the field. The volume is complete with a detailed classification system for known classes of astronomical objects, offering students, researchers and amateur observers a valuable reference and guide.

Written by leading experts in the field, Stellar Spectral Classification is the only book to comprehensively discuss both the foundations and most up-to-date techniques of MK and other spectral classification systems. Definitive and encyclopedic, the book introduces the astrophysics of spectroscopy, reviews the entire field of stellar astronomy, and shows how the well-tested methods of spectral classification are a powerful discovery tool for graduate students and researchers working in astronomy and astrophysics. The book begins with a historical survey, followed by chapters discussing the entire range of stellar phenomena, from brown dwarfs to supernovae. The authors account for advances in the field, including the addition of the L and T dwarf classes; the revision of the carbon star, Wolf-Rayet, and white dwarf classification schemes; and the application of neural nets to spectral classification. Copious figures illustrate the morphology of stellar spectra, and the book incorporates recent discoveries from earth-based and satellite data. Many examples of spectra are given in the red, ultraviolet, and infrared regions, as well as in the traditional blue-violet optical region, all of which are useful for researchers identifying stellar and galactic spectra. This essential reference includes a glossary, handy appendixes and tables, an index, and a Web-based resource of spectra. In addition to the authors, the contributors are Adam J. Burgasser, Margaret M. Hanson, J. Davy Kirkpatrick, and Nolan R. Walborn.

Since the invention of the telescope 400 years ago, astronomers have rapidly discovered countless celestial objects. But how does one make sense of it all? Astronomer and former NASA Chief Historian Steven J. Dick brings order to this menagerie by defining 82 classes of astronomical objects, which he places in a beginner-friendly system known as "Astronomy’s Three Kingdoms.” Rather than concentrating on technicalities, this system focuses on the history of each object, the nature of its discovery, and our current knowledge about it. The ensuing book can therefore be read on at least two levels. On one level, it is an illustrated guide to various types of astronomical wonders. On another level, it is considerably more: the first comprehensive classification system to cover all celestial objects in a consistent manner. Accompanying each spread are spectacular historical and modern images. The result is a pedagogical tour-de-force, whereby readers can easily master astronomy’s three realms of planets, stars, and galaxies.

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

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, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Astronomical surveys produce large amounts of photometric, spectroscopic and time-series data. Object classification, parameter determination, novelty detection and the discovery of structure in these are challenging tasks. This book, featuring contributions from both astronomers and computer scientists, discusses a broad range of astronomical problems and shows how various machine learining and statistical analysis techniques are being used to solve them.