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.
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)
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 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys 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, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded 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 astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers
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
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 744 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
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.
Download or read book Modeling Machine Learning and Astronomy written by Snehanshu Saha and published by . This book was released on 2020 with total page 185 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.
Download or read book Machine Learning for Small Bodies in the Solar System written by Valerio Carruba and published by Elsevier. This book was released on 2025-01-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, detection algorithms, etc. Allowing readers to apply ML and AI to the study of asteroids, comets, moons, and Trans-Neptunian Objects. The practical approach encompasses a wide range of topics, providing both experienced and novice researchers with essential tools and insights. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working into the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on Methodologies and techniques to apply ML and AI methodologies.
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
Download or read book Bayesian Models for Astrophysical Data written by Joseph M. Hilbe and published by Cambridge University Press. This book was released on 2017-04-27 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.
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.
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"--
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.
Download or read book Artificial Intelligence for Space AI4SPACE written by Matteo Madi and published by CRC Press. This book was released on 2023-12-18 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new age space value chain is a complex interconnected system with diverse actors, which involves cross-sector and cross-border collaborations. This book helps to enrich the knowledge of Artificial Intelligence (AI) across the value chain in the space-related domains. Advancements of AI and Machine Learning have impactfully supported the space sector transformation as it is shown in the book. "This book embarks on a journey through the fascinating realm of AI in space, exploring its profound implications, emerging trends, and transformative potential." Prof. Dr. Oliver Ullrich - Director Innovation Cluster Space and Aviaton (UZH Space Hub), University of Zurich, Switzerland Aimed at space engineers, risk analysts, policy makers, technical experts and non-specialists, this book demonstrates insights into the implementation of AI in the space sector, alongside its limitations and use-case examples. It covers diverse AI-related topics applicable to space technologies or space big data such as AI-based technologies for improving Earth Observation big data, AI for space robotics exploration, AI for astrophysics, AI for emerging in-orbit servicing market, and AI for space tourism safety improvement. Key Features: Provides an interdisciplinary approach, with chapter contributions from expert teams working in the governmental or private space sectors, with valuable contributions from computer scientists and legal experts Presents insights into AI implementation and how to unlock AI technologies in the field Up-to-date with the latest developments and cutting-edge applications
Download or read book Modeling the 3D Milky Way Using Machine Learning with Gaia and Infrared Surveys written by David Cornu (Astrophysicien).) and published by . This book was released on 2020 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Artificial Intelligence Models for the Dark Universe written by Ariel Fernández and published by CRC Press. This book was released on 2024-08-20 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all the matter and energetic equivalent in the universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem and the apparent distortions in the dynamics of deep space, and so coming to grips with the invisible universe has become a scientific imperative. This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to unveil the secrets of the dark universe. Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implement a physical model of the dark sector that enables a meaningful extrapolation into the visibile sector. The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics, and computer science focusing on AI applications to elucidate the nature of the dark universe. Key Features: · Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy. · Up to date with the latest cutting-edge research. · Authored by an expert on artificial intelligence and mathematical physics.
