Claude A. Pruneau

Data Analysis Techniques for Physical Scientists (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 5. Oktober 2017
• Englisch
• ISBN-13: 9781108266062
• Artikelnr.: 49265452

Autorenporträt

Claude A. Pruneau is a Professor of Physics at the Wayne State University, Michigan, from where he received the 2006 Excellence in Teaching Presidential Award. He is also a member of the ALICE collaboration, and conducts an active research program in the study of the Quark Gluon Plasma produced in relativistic heavy ion collisions at the CERN Large Hadron Collider. He has worked as a Research Fellow at both Atomic Energy for Canada Limited and McGill University, Canada, and is a member of the American Physical Society, Canadian Association of Physicists and the Union of Concerned Scientists.

Inhaltsangabe

Preface
How to read this book
1. The scientific method
Part I. Foundation in Probability and Statistics: 2. Probability
3. Probability models
4. Classical inference I: estimators
5. Classical inference II: optimization
6. Classical inference III: confidence intervals and statistical tests
7. Bayesian inference
Part II. Measurement Techniques: 8. Basic measurements
9. Event reconstruction
10. Correlation functions
11. The multiple facets of correlation functions
12. Data correction methods
Part III. Simulation Techniques: 13. Monte Carlo methods
14. Collision and detector modeling
List of references
Index.