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Data Analysis in High Energy Physics

2013-08-30 Science
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Release : 2013-08-30
Genre : Science
Kind : eBook
Book Rating : 430/5 ( reviews)

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Book Synopsis Data Analysis in High Energy Physics by : Olaf Behnke

Download or read book Data Analysis in High Energy Physics written by Olaf Behnke. This book was released on 2013-08-30. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice for typical questions and problems. The basic methods for inferring results from data are presented as well as tools for advanced tasks such as improving the signal-to-background ratio, correcting detector effects, determining systematics and many others. Concrete applications are discussed in analysis walkthroughs. Each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links. The book targets a broad readership at all career levels - from students to senior researchers. An accompanying website provides more algorithms as well as up-to-date information and links. * Free solutions manual available for lecturers at www.wiley-vch.de/supplements/

Data Analysis Techniques for High-Energy Physics

2000-08-17 Medical
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Release : 2000-08-17
Genre : Medical
Kind : eBook
Book Rating : 486/5 ( reviews)

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Book Synopsis Data Analysis Techniques for High-Energy Physics by : Rudolf Frühwirth

Download or read book Data Analysis Techniques for High-Energy Physics written by Rudolf Frühwirth. This book was released on 2000-08-17. Available in PDF, EPUB and Kindle. Book excerpt: Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments. The observation of particle interactions involves the analysis of large and complex data samples. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a sometimes huge background. The use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects like particle tracks or showers is then examined and the track and vertex fitting methods necessary to extract the maximum amount of information from the available measurements are explained. The final chapter describes tools and methods which are useful to the experimenter in the physical interpretation and in the presentation of the results. This indispensable guide will appeal to graduate students, researchers and computer and electronic engineers involved with experimental physics.

Statistical Analysis Techniques in Particle Physics

2013-10-24 Science
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Release : 2013-10-24
Genre : Science
Kind : eBook
Book Rating : 291/5 ( reviews)

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Book Synopsis Statistical Analysis Techniques in Particle Physics by : Ilya Narsky

Download or read book Statistical Analysis Techniques in Particle Physics written by Ilya Narsky. This book was released on 2013-10-24. Available in PDF, EPUB and Kindle. Book excerpt: Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.

Data Analysis Techniques for Physical Scientists

2017-10-05 Science
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Release : 2017-10-05
Genre : Science
Kind : eBook
Book Rating : 882/5 ( reviews)

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Book Synopsis Data Analysis Techniques for Physical Scientists by : Claude A. Pruneau

Download or read book Data Analysis Techniques for Physical Scientists written by Claude A. Pruneau. This book was released on 2017-10-05. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Methods for Data Analysis in Particle Physics

2017-10-13 Science
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Release : 2017-10-13
Genre : Science
Kind : eBook
Book Rating : 402/5 ( reviews)

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Book Synopsis Statistical Methods for Data Analysis in Particle Physics by : Luca Lista

Download or read book Statistical Methods for Data Analysis in Particle Physics written by Luca Lista. This book was released on 2017-10-13. Available in PDF, EPUB and Kindle. Book excerpt: This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation).

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