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data science in astrophysics: Statistics, Data Mining, and Machine Learning in Astronomy Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, 2014-01-12 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 |
data science in astrophysics: Data Analysis in Astronomy V. di Gesù, L. Scarsi, P. Crane, J.H. Friedman, S. Levialdi, 2012-12-06 The international Workshop on Data Analysis in Astronomy was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G. De Biase, Prof. G. Sedmak, Prof. A. Zichichi and of the local organizing committee Dr. R. Buccheri and Dr. M.C. Macca rone together with Miss P. Savalli and Dr. A. Gabriele of the E. Majo rana Center for their support and the unvaluable part in arranging the Workshop. |
data science in astrophysics: Advances in Machine Learning and Data Mining for Astronomy Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava, 2012-03-29 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 |
data science in astrophysics: Knowledge Discovery in Big Data from Astronomy and Earth Observation Petr Skoda, Fathalrahman Adam, 2020-04-10 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 |
data science in astrophysics: Big Data in Astronomy Linghe Kong, Tian Huang, Yongxin Zhu, Shenghua Yu, 2020-06-13 Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world's largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. - Bridges the gap between radio astronomy and computer science - Includes coverage of the observation lifecycle as well as data collection, processing and analysis - Presents state-of-the-art research and techniques in big data related to radio astronomy - Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST) |
data science in astrophysics: Statistical Methods for Astronomical Data Analysis Asis Kumar Chattopadhyay, Tanuka Chattopadhyay, 2014-10-01 This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors. |
data science in astrophysics: Astronomical Image and Data Analysis J.-L. Starck, F. Murtagh, 2007-06-21 With information and scale as central themes, this comprehensive survey explains how to handle real problems in astronomical data analysis using a modern arsenal of powerful techniques. It treats those innovative methods of image, signal, and data processing that are proving to be both effective and widely relevant. The authors are leaders in this rapidly developing field and draw upon decades of experience. They have been playing leading roles in international projects such as the Virtual Observatory and the Grid. The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining. The coverage includes chapters or appendices on: detection and filtering; image compression; multichannel, multiscale, and catalog data analytical methods; wavelets transforms, Picard iteration, and software tools. This second edition of Starck and Murtagh's highly appreciated reference again deals with topics that are at or beyond the state of the art. It presents material which is more algorithmically oriented than most alternatives and broaches new areas like ridgelet and curvelet transforms. Throughout the book various additions and updates have been made. |
data science in astrophysics: Intelligent Astrophysics Ivan Zelinka, Massimo Brescia, Dalya Baron, 2021-04-15 This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and information content of our universe, the use of tessellation in astronomy, characterization and classification of astronomical phenomena, identification of extragalactic objects, classification of pulsars and many other interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics. |
data science in astrophysics: Astronomy and Astrophysics in the New Millennium National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Board on Physics and Astronomy, Astronomy and Astrophysics Survey Committee, 2002-02-07 In preparing the report, Astronomy and Astrophysics in the New Millenium , the AASC made use of a series of panel reports that address various aspects of ground- and space-based astronomy and astrophysics. These reports provide in-depth technical detail. Astronomy and Astrophysics in the New Millenium: An Overview summarizes the science goals and recommended initiatives in a short, richly illustrated, non-technical booklet. |
data science in astrophysics: New Worlds, New Horizons in Astronomy and Astrophysics National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Board on Physics and Astronomy, Committee for a Decadal Survey of Astronomy and Astrophysics, 2011-02-04 Driven by discoveries, and enabled by leaps in technology and imagination, our understanding of the universe has changed dramatically during the course of the last few decades. The fields of astronomy and astrophysics are making new connections to physics, chemistry, biology, and computer science. Based on a broad and comprehensive survey of scientific opportunities, infrastructure, and organization in a national and international context, New Worlds, New Horizons in Astronomy and Astrophysics outlines a plan for ground- and space- based astronomy and astrophysics for the decade of the 2010's. Realizing these scientific opportunities is contingent upon maintaining and strengthening the foundations of the research enterprise including technological development, theory, computation and data handling, laboratory experiments, and human resources. New Worlds, New Horizons in Astronomy and Astrophysics proposes enhancing innovative but moderate-cost programs in space and on the ground that will enable the community to respond rapidly and flexibly to new scientific discoveries. The book recommends beginning construction on survey telescopes in space and on the ground to investigate the nature of dark energy, as well as the next generation of large ground-based giant optical telescopes and a new class of space-based gravitational observatory to observe the merging of distant black holes and precisely test theories of gravity. New Worlds, New Horizons in Astronomy and Astrophysics recommends a balanced and executable program that will support research surrounding the most profound questions about the cosmos. The discoveries ahead will facilitate the search for habitable planets, shed light on dark energy and dark matter, and aid our understanding of the history of the universe and how the earliest stars and galaxies formed. The book is a useful resource for agencies supporting the field of astronomy and astrophysics, the Congressional committees with jurisdiction over those agencies, the scientific community, and the public. |
data science in astrophysics: Modern Statistical Methods for Astronomy Eric D. Feigelson, G. Jogesh Babu, 2012-07-12 Modern Statistical Methods for Astronomy: With R Applications. |
data science in astrophysics: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
data science in astrophysics: Bayesian Models for Astrophysical Data Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida, 2017-04-27 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. |
data science in astrophysics: Machine Learning and Data Science in the Power Generation Industry Patrick Bangert, 2021-01-14 Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls |
data science in astrophysics: Data Analysis in Cosmology Vicent J. Martinez, Enn Saar, Enrique Martinez Gonzales, Maria Jesus Pons-Borderia, 2009-07-09 The amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe. |
data science in astrophysics: Numerical Python in Astronomy and Astrophysics Wolfram Schmidt, Marcel Völschow, 2021-07-14 This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks. |
data science in astrophysics: Data Analysis Techniques for Physical Scientists Claude A. Pruneau, 2017-10-05 A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques. |
data science in astrophysics: Laboratory Astrophysics Guillermo M. Muñoz Caro, Rafael Escribano, 2018-10-11 This book focuses on the most recent, relevant, comprehensive and significant aspects in the well-established multidisciplinary field Laboratory Astrophysics. It focuses on astrophysical environments, which include asteroids, comets, the interstellar medium, and circumstellar and circumplanetary regions. Its scope lies between physics and chemistry, since it explores physical properties of the gas, ice, and dust present in those systems, as well as chemical reactions occurring in the gas phase, the bare dust surface, or in the ice bulk and its surface. Each chapter provides the necessary mathematical background to understand the subject, followed by a case study of the corresponding system. The book provides adequate material to help interpret the observations, or the computer models of astrophysical environments. It introduces and describes the use of spectroscopic tools for laboratory astrophysics. This book is mainly addressed to PhD graduates working in this field or observers and modelers searching for information on ice and dust processes. |
data science in astrophysics: Essential Radio Astronomy James J. Condon, Scott M. Ransom, 2016-04-05 The ideal text for a one-semester course in radio astronomy Essential Radio Astronomy is the only textbook on the subject specifically designed for a one-semester introductory course for advanced undergraduates or graduate students in astronomy and astrophysics. It starts from first principles in order to fill gaps in students' backgrounds, make teaching easier for professors who are not expert radio astronomers, and provide a useful reference to the essential equations used by practitioners. This unique textbook reflects the fact that students of multiwavelength astronomy typically can afford to spend only one semester studying the observational techniques particular to each wavelength band. Essential Radio Astronomy presents only the most crucial concepts—succinctly and accessibly. It covers the general principles behind radio telescopes, receivers, and digital backends without getting bogged down in engineering details. Emphasizing the physical processes in radio sources, the book's approach is shaped by the view that radio astrophysics owes more to thermodynamics than electromagnetism. Proven in the classroom and generously illustrated throughout, Essential Radio Astronomy is an invaluable resource for students and researchers alike. The only textbook specifically designed for a one-semester course in radio astronomy Starts from first principles Makes teaching easier for astronomy professors who are not expert radio astronomers Emphasizes the physical processes in radio sources Covers the principles behind radio telescopes and receivers Provides the essential equations and fundamental constants used by practitioners Supplementary website includes lecture notes, problem sets, exams, and links to interactive demonstrations An online illustration package is available to professors |
data science in astrophysics: Machine Learning for Planetary Science Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner, 2022-03-22 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 |
data science in astrophysics: Data Science Vijay Kotu, Bala Deshpande, 2018-11-27 Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner |
data science in astrophysics: Science With The Cherenkov Telescope Array The Cta Consortium, 2018-12-31 This book summarizes the science to be carried out by the upcoming Cherenkov Telescope Array, a major ground-based gamma-ray observatory that will be constructed over the next six to eight years. The major scientific themes, as well as core program of key science projects, have been developed by the CTA Consortium, a collaboration of scientists from many institutions worldwide.CTA will be the major facility in high-energy and very high-energy photon astronomy over the next decade and beyond. CTA will have capabilities well beyond past and present observatories. Thus, CTA's science program is expected to be rich and broad and will complement other major multiwavelength and multimessenger facilities. This book is intended to be the primary resource for the science case for CTA and it thus will be of great interest to the broader physics and astronomy communities. The electronic version (e-book) is available in open access. |
data science in astrophysics: Asteroseismic Data Analysis Sarbani Basu, William J. Chaplin, 2017-09-05 Studies of stars and stellar populations, and the discovery and characterization of exoplanets, are being revolutionized by new satellite and telescope observations of unprecedented quality and scope. Some of the most significant advances have been in the field of asteroseismology, the study of stars by observation of their oscillations. Asteroseismic Data Analysis gives a comprehensive technical introduction to this discipline. This book not only helps students and researchers learn about asteroseismology; it also serves as an essential instruction manual for those entering the field. The book presents readers with the foundational techniques used in the analysis and interpretation of asteroseismic data on cool stars that show solar-like oscillations. The techniques have been refined, and in some cases developed, to analyze asteroseismic data collected by the NASA Kepler mission. Topics range from the analysis of time-series observations to extract seismic data for stars to the use of those data to determine global and internal properties of the stars. Reading lists and problem sets are provided, and data necessary for the problem sets are available online. The first book to describe in detail the different techniques used to analyze the data on stellar oscillations, Asteroseismic Data Analysis offers an invaluable window into the hearts of stars. Introduces the asteroseismic study of stars and the theory of stellar oscillations Describes the analysis of observational (time-domain) data Examines how seismic parameters are extracted from observations Explores how stellar properties are determined from seismic data Looks at the “inverse problem,” where frequencies are used to infer internal structures of stars |
data science in astrophysics: Dark Matter and Dark Energy Sabino Matarrese, Monica Colpi, Vittorio Gorini, Ugo Moschella, 2011-02-10 This book brings together reviews from leading international authorities on the developments in the study of dark matter and dark energy, as seen from both their cosmological and particle physics side. Studying the physical and astrophysical properties of the dark components of our Universe is a crucial step towards the ultimate goal of unveiling their nature. The work developed from a doctoral school sponsored by the Italian Society of General Relativity and Gravitation. The book starts with a concise introduction to the standard cosmological model, as well as with a presentation of the theory of linear perturbations around a homogeneous and isotropic background. It covers the particle physics and cosmological aspects of dark matter and (dynamical) dark energy, including a discussion of how modified theories of gravity could provide a possible candidate for dark energy. A detailed presentation is also given of the possible ways of testing the theory in terms of cosmic microwave background, galaxy redshift surveys and weak gravitational lensing observations. Included is a chapter reviewing extensively the direct and indirect methods of detection of the hypothetical dark matter particles. Also included is a self-contained introduction to the techniques and most important results of numerical (e.g. N-body) simulations in cosmology. This volume will be useful to researchers, PhD and graduate students in Astrophysics, Cosmology Physics and Mathematics, who are interested in cosmology, dark matter and dark energy. |
data science in astrophysics: Perspectives on Data Science for Software Engineering Tim Menzies, Laurie Williams, Thomas Zimmermann, 2016-07-14 Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. - Presents the wisdom of community experts, derived from a summit on software analytics - Provides contributed chapters that share discrete ideas and technique from the trenches - Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data - Presented in clear chapters designed to be applicable across many domains |
data science in astrophysics: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
data science in astrophysics: Pathways to Discovery in Astronomy and Astrophysics for the 2020s National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Board on Physics and Astronomy, Space Studies Board, Decadal Survey on Astronomy and Astrophysics 2020 (Astro2020), 2022-08-04 The steering committee was specifically asked to (1) provide an overview of the current state of astronomy and astrophysics science, and technology research in support of that science, with connections to other scientific areas where appropriate; (2) identify the most compelling science challenges and frontiers in astronomy and astrophysics, which shall motivate the committee’s strategy for the future; (3) develop a comprehensive research strategy to advance the frontiers of astronomy and astrophysics for the period 2022-2032 that will include identifying, recommending, and ranking the highest-priority research activities; (4) utilize and recommend decision rules, where appropriate, that can accommodate significant but reasonable deviations in the projected budget or changes in urgency precipitated by new discoveries or unanticipated competitive activities; (5) assess the state of the profession, including workforce and demographic issues in the field, identify areas of concern and importance to the community, and where possible, provide specific, actionable, and practical recommendations to the agencies and community to address these areas. This report proposes a broad, integrated plan for space- and ground-based astronomy and astrophysics for the decade 2023-2032. It also lays the foundations for further advances in the following decade. |
data science in astrophysics: Machine Learning and Data Science in the Oil and Gas Industry Patrick Bangert, 2021-03-04 Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not) |
data science in astrophysics: Advanced Astrophysics Nebojsa Duric, 2004 This 2003 book develops the basic underlying physics required for a fuller, richer understanding of the science of astrophysics and the important astronomical phenomena it describes. The cosmos manifests phenomena in which physics can appear in its most extreme, and therefore more insightful, forms. A proper understanding of phenomena like black holes, quasars and extrasolar planets requires that we understand the physics that underlies all of astrophysics. Consequently, developing astrophysical concepts from fundamental physics has the potential to achieve two goals: to derive a better understanding of astrophysical phenomena from first principles and to illuminate the physics from which the astrophysics is developed. To that end, astrophysical topics are grouped according to the relevant areas of physics. The book is ideal as a text for graduate and advanced undergraduate students as well as a reference for established researchers. |
data science in astrophysics: Astrophysics Through Computation Brian Koberlein, David Meisel, 2013-06-28 This new astrophysics text integrates analytical and computational methods to explore a broad range of topics in astrophysics. |
data science in astrophysics: The Edge of the Sky Roberto Trotta, 2014-09-23 From the big bang to black holes, from dark matter to dark energy, from the origins of the universe to its ultimate destiny, The Edge of the Sky tells the story of the most important discoveries and mysteries in modern cosmology—with a twist. The book’s lexicon is limited to the thousand most common words in the English language, excluding physics, energy, galaxy, or even universe. Through the eyes of a fictional scientist (Student-People) hunting for dark matter with one of the biggest telescopes (Big-Seers) on Earth (Home-World), cosmologist Roberto Trotta explores the most important ideas about our universe (All-there-is) in language simple enough for anyone to understand. A unique blend of literary experimentation and science popularization, this delightful book is a perfect gift for any aspiring astronomer. The Edge of the Sky tells the story of the universe on a human scale, and the result is out of this world. |
data science in astrophysics: Astrostatistics Gutti Jogesh Babu, E.D. Feigelson, 1996-08-01 Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The marriage of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields. |
data science in astrophysics: The First Galaxies in the Universe Abraham Loeb, Steven R. Furlanetto, 2013-01-15 This book provides a comprehensive, self-contained introduction to one of the most exciting frontiers in astrophysics today: the quest to understand how the oldest and most distant galaxies in our universe first formed. Until now, most research on this question has been theoretical, but the next few years will bring about a new generation of large telescopes that promise to supply a flood of data about the infant universe during its first billion years after the big bang. This book bridges the gap between theory and observation. It is an invaluable reference for students and researchers on early galaxies. The First Galaxies in the Universe starts from basic physical principles before moving on to more advanced material. Topics include the gravitational growth of structure, the intergalactic medium, the formation and evolution of the first stars and black holes, feedback and galaxy evolution, reionization, 21-cm cosmology, and more. Provides a comprehensive introduction to this exciting frontier in astrophysics Begins from first principles Covers advanced topics such as the first stars and 21-cm cosmology Prepares students for research using the next generation of large telescopes Discusses many open questions to be explored in the coming decade |
data science in astrophysics: Grading NASA's Solar System Exploration Program National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Assessing the Solar System Exploration Program, 2008-04-25 The NASA Authorization Act of 2005 directed the agency to ask the NRC to assess the performance of each division in the NASA Science directorate at five-year intervals. In this connection, NASA requested the NRC to review the progress the Planetary Exploration Division has made in implementing recommendations from previous, relevant NRC studies. This book provides an assessment of NASA's progress in fulfilling those recommendations including an evaluation how well it is doing and of current trends. The book covers key science questions, flight missions, Mars exploration, research and analysis, and enabling technologies. Recommendations are provided for those areas in particular need of improvement. |
data science in astrophysics: Unsolved Problems in Astrophysics John N. Bahcall, Jeremiah P. Ostriker, 1997-02-06 The field of astrophysics is in the midst of a technological renaissance. The emphasis of this collection of essays, composed by a stellar group of astronomers and astrophysicists, is on the current state of our knowledge as a preparation for future unraveling of more mysteries of the universe, which appear most amenable to solution. Aspiring atrophysicists will be enthralled. |
data science in astrophysics: The Disordered Cosmos Chanda Prescod-Weinstein, 2021-03-09 From a star theoretical physicist, a journey into the world of particle physics and the cosmos—and a call for a more liberatory practice of science. Winner of the 2021 Los Angeles Times Book Prize in Science & Technology A Finalist for the 2022 PEN/E.O. Wilson Literary Science Writing Award A Smithsonian Magazine Best Science Book of 2021 A Symmetry Magazine Top 10 Physics Book of 2021 An Entropy Magazine Best Nonfiction Book of 2020-2021 A Publishers Weekly Best Nonfiction Book of the Year A Kirkus Reviews Best Nonfiction Book of 2021 A Booklist Top 10 Sci-Tech Book of the Year In The Disordered Cosmos, Dr. Chanda Prescod-Weinstein shares her love for physics, from the Standard Model of Particle Physics and what lies beyond it, to the physics of melanin in skin, to the latest theories of dark matter—along with a perspective informed by history, politics, and the wisdom of Star Trek. One of the leading physicists of her generation, Dr. Chanda Prescod-Weinstein is also one of fewer than one hundred Black American women to earn a PhD from a department of physics. Her vision of the cosmos is vibrant, buoyantly nontraditional, and grounded in Black and queer feminist lineages. Dr. Prescod-Weinstein urges us to recognize how science, like most fields, is rife with racism, misogyny, and other forms of oppression. She lays out a bold new approach to science and society, beginning with the belief that we all have a fundamental right to know and love the night sky. The Disordered Cosmos dreams into existence a world that allows everyone to experience and understand the wonders of the universe. |
data science in astrophysics: A Companion to Astronomy and Astrophysics Kenneth R. Lang, 2007-01-15 Astronomy and Astrophysics is a comprehensive, fundamental, and up-to-date reference book. It is filled with vital information and basic facts for amateur astronomers and professional astrophysicists, and for anyone interested in the Universe, from the Earth and other planets to the stars, galaxies and beyond. An exceptionally thorough Index cross-references concepts, discoveries and individuals in both the Timeline section and Dictionary section. The combined result is a unique stand-alone reference volume in which the reader can quickly locate information, while also discovering new and unexpected knowledge. |
data science in astrophysics: Astrophysics for Young People in a Hurry Neil deGrasse Tyson, 2019-02-05 Neil deGrasse Tyson’s #1 New York Times best-selling guide to the cosmos, adapted for young readers. From the basics of physics to big questions about the nature of space and time, celebrated astrophysicist and science communicator Neil deGrasse Tyson breaks down the mysteries of the cosmos into bite-sized pieces. Astrophysics for Young People in a Hurry describes the fundamental rules and unknowns of our universe clearly—and with Tyson’s characteristic wit, there’s a lot of fun thrown in, too. This adaptation by Gregory Mone includes full-color photos, infographics, and extra explanations to make even the trickiest concepts accessible. Building on the wonder inspired by outer space, Astrophysics for Young People in a Hurry introduces an exciting field and the principles of scientific inquiry to young readers. |
data science in astrophysics: Machine Learning for Astrophysics Filomena Bufano, Simone Riggi, Eva Sciacca, Francesco Schilliro, 2023-11-15 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. |
data science in astrophysics: The Crowd & the Cosmos Chris Lintott, 2019 Astronomer and TV presenter Chris Lintott tells the story of the Zooniverse, the platform which enables hundreds of thousands of ordinary people to contribute to scientific research. He describes the discoveries they are making, and shows how, in the world of Big Data and smart machines, humans play a unique part in adding to scientific knowledge. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …