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Data Analysis In High Energy Physics

Author: Olaf Behnke
Publisher: John Wiley & Sons
ISBN: 3527653430
Size: 54.53 MB
Format: PDF, ePub, Mobi
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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 In High Energy Physics

Author: Olaf Behnke
Publisher: John Wiley & Sons
ISBN: 9783527410583
Size: 23.21 MB
Format: PDF, Docs
View: 4824
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This practical guide covers the most essential statistics-related tasks and problems encountered in high-energy physics data analyses. It addresses both advanced students entering the field of particle physics as well as researchers looking for a reliable source on optimal separation of signal and background, determining signals or estimating upper limits, correcting the data for detector effects and evaluating systematic uncertainties. Each chapter is dedicated to a single topic and supplemented by a substantial number of both paper and computer exercises related to real experiments, with the solutions provided at the end of the book along with references. A special feature of the book are the analysis walk-throughs used to illustrate the application of the methods discussed beforehand. The authors give examples of data analysis, referring to real problems in HEP, and display the different stages of data analysis in a descriptive manner. The accompanying website provides more algorithms as well as up- to-date information and links.

Data Analysis In High Energy Physics

Author: Olaf Behnke
Publisher: John Wiley & Sons
ISBN: 3527653449
Size: 21.15 MB
Format: PDF, Mobi
View: 3233
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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/

Statistical Analysis Techniques In Particle Physics

Author: Ilya Narsky
Publisher: John Wiley & Sons
ISBN: 3527677291
Size: 27.55 MB
Format: PDF
View: 2366
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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.

Statistics For Nuclear And Particle Physicists

Author: Louis Lyons
Publisher: Cambridge University Press
ISBN: 9780521379342
Size: 18.52 MB
Format: PDF, Docs
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This practical approach to statistical problems arising regularly in analyzing data from nuclear and high energy physics experiments is geared toward non-statisticians.

Statistical Methods For Data Analysis In Particle Physics

Author: Luca Lista
Publisher: Springer
ISBN: 3319628402
Size: 10.62 MB
Format: PDF, Mobi
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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).

Introduction To Experimental Particle Physics

Author: Richard C. Fernow
Publisher: Cambridge University Press
ISBN: 9780521379403
Size: 59.15 MB
Format: PDF, Mobi
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A concise, balanced overview of the most important topics in experimental particle physics of the past forty years is presented in this comprehensive study. Beginning with a review of particle physics and electromagnetic and nuclear interactions, the book continues with a discussion of three nearly universal aspects of particle physics experiments: beams, targets and fast electrons. The second part of the text details the properties of various types of particle detectors, such as scintillation and Cerenkov counters, proportional and drift chambers, sampling calorimeters, and special detectors. The last chapter demonstrates how the various aspects of the previous chapters can be integrated in an experimental system. There are numerous references to the research literature, in addition to examples and workable problems.

Techniques For Nuclear And Particle Physics Experiments

Author: William R. Leo
Publisher: Springer Science & Business Media
ISBN: 3642579205
Size: 35.21 MB
Format: PDF, ePub, Docs
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A treatment of the experimental techniques and instrumentation most often used in nuclear and particle physics experiments as well as in various other experiments, providing useful results and formulae, technical know-how and informative details. This second edition has been revised, while sections on Cherenkov radiation and radiation protection have been updated and extended.

Statistical Data Analysis

Author: Glen Cowan
Publisher: Oxford University Press
ISBN: 0198501560
Size: 59.36 MB
Format: PDF, Docs
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This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

Fundamentals Of Digital Image Processing

Author: Chris Solomon
Publisher: John Wiley & Sons
ISBN: 1119957001
Size: 16.57 MB
Format: PDF, ePub, Docs
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This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) . Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples. Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.