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Author : Ben Henghes
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (131 download)
Download or read book Novel Applications of Machine Learning in Astronomy and Beyond written by Ben Henghes and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael J. Way
Publisher : CRC Press
ISBN 13 : 143984173X
Total Pages : 746 pages
Book Rating : 4.4/5 (398 download)
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Sven Dennis Kügler
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (944 download)
Download or read book On the Application of Machine Learning Approaches in Astronomy written by Sven Dennis Kügler and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Viviana Acquaviva
Publisher : Princeton University Press
ISBN 13 : 0691206414
Total Pages : 280 pages
Book Rating : 4.6/5 (912 download)
Download or read book Machine Learning for Physics and Astronomy written by Viviana Acquaviva and published by Princeton University Press. This book was released on 2023-08-15 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on introduction to machine learning and its applications to the physical sciences As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts Includes a wealth of review questions and quizzes Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics Accessible to self-learners with a basic knowledge of linear algebra and calculus Slides and assessment questions (available only to instructors)
Author : Željko Ivezić
Publisher : Princeton University Press
ISBN 13 : 0691151687
Total Pages : 550 pages
Book Rating : 4.6/5 (911 download)
Download or read book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2014-01-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
Author : Long Xu
Publisher : Springer Nature
ISBN 13 : 9811927464
Total Pages : 103 pages
Book Rating : 4.8/5 (119 download)
Download or read book Deep Learning in Solar Astronomy written by Long Xu and published by Springer Nature. This book was released on 2022-05-27 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author : Željko Ivezić
Publisher : Princeton University Press
ISBN 13 : 0691198306
Total Pages : 548 pages
Book Rating : 4.6/5 (911 download)
Download or read book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2019-12-03 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: "As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest"--
Author : Filomena Bufano
Publisher : Springer Nature
ISBN 13 : 3031341678
Total Pages : 206 pages
Book Rating : 4.0/5 (313 download)
Download or read book Machine Learning for Astrophysics written by Filomena Bufano and published by Springer Nature. This book was released on 2023-11-15 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
Author : Joern Helbert
Publisher : Elsevier
ISBN 13 : 0128187220
Total Pages : 234 pages
Book Rating : 4.1/5 (281 download)
Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice
Author : Martin Erdmann
Publisher : World Scientific
ISBN 13 : 9811237476
Total Pages : 340 pages
Book Rating : 4.8/5 (112 download)
Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Author : Kana Moriwaki
Publisher : Springer Nature
ISBN 13 : 9811958807
Total Pages : 126 pages
Book Rating : 4.8/5 (119 download)
Download or read book Large-Scale Structure of the Universe written by Kana Moriwaki and published by Springer Nature. This book was released on 2022-11-01 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Line intensity mapping (LIM) is an observational technique that probes the large-scale structure of the Universe by collecting light from a wide field of the sky. This book demonstrates a novel analysis method for LIM using machine learning (ML) technologies. The author develops a conditional generative adversarial network that separates designated emission signals from sources at different epochs. It thus provides, for the first time, an efficient way to extract signals from LIM data with foreground noise. The method is complementary to conventional statistical methods such as cross-correlation analysis. When applied to three-dimensional LIM data with wavelength information, high reproducibility is achieved under realistic conditions. The book further investigates how the trained machine extracts the signals, and discusses the limitation of the ML methods. Lastly an application of the LIM data to a study of cosmic reionization is presented. This book benefits students and researchers who are interested in using machine learning to multi-dimensional data not only in astronomy but also in general applications.
Author : Mary Kay Carson
Publisher : Chicago Review Press
ISBN 13 : 1613745478
Total Pages : 137 pages
Book Rating : 4.6/5 (137 download)
Download or read book Beyond the Solar System written by Mary Kay Carson and published by Chicago Review Press. This book was released on 2013-06 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracing the evolution of humankind's pursuit of astronomical knowledge, this resource looks deep into the furthest reaches of space. Children will follow along as the realization that the Earth is not at the center of the universe leads all the way up to recent telescopic proof of planets orbiting stars outside the solar system. In addition to its engaging history, this book contains 21 hands-on projects to further explore the subjects discussed. Readers will build a three-dimensional representation of the constellation Orion, see how the universe expands using an inflating balloon, and construct a reflecting telescope out of a makeup mirror and a magnifying glass. It also includes small biographies of famous astronomers, a time line of major scientific discoveries, a glossary of technical terms, and dozens of full-color images taken by the Hubble Space Telescope and the Chandra X-Ray Observatory.
Author : Snehanshu Saha
Publisher : Springer Nature
ISBN 13 : 9813364637
Total Pages : 193 pages
Book Rating : 4.8/5 (133 download)
Download or read book Modeling, Machine Learning and Astronomy written by Snehanshu Saha and published by Springer Nature. This book was released on 2021-01-12 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the First International Conference on Modeling, Machine Learning and Astronomy, MMLA 2019, held in Bangalore, India, in November 2019. The 11 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 63 submissions. They are organized in topical sections on modeling and foundations; machine learning applications; astronomy and astroinformatics.
Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 162 pages
Book Rating : 4./5 ( download)
Download or read book Master Machine Learning Algorithms written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-03-04 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Author : Željko Ivezić
Publisher :
ISBN 13 :
Total Pages : 552 pages
Book Rating : 4.:/5 (11 download)
Download or read book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić and published by . This book was released on 2014 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers.
Author : Gerard Peter Kuiper
Publisher :
ISBN 13 :
Total Pages : 601 pages
Book Rating : 4.:/5 (54 download)
Download or read book Planets and Satellites written by Gerard Peter Kuiper and published by . This book was released on 1961 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt:
