Data Analysis Techniques for Physical Scientists

A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods.

Claude A. Pruneau (Author)

9781108416788, Cambridge University Press

Hardback, published 5 October 2017

716 pages, 195 b/w illus. 20 tables
25.3 x 19.3 x 3.5 cm, 1.7 kg

'Data Analysis Techniques for Physical Scientists offers an accessible but rigorous and comprehensive presentation of data analysis techniques in modern large-scale experiments. Furthermore, much of the book is applicable beyond the physical sciences; it is a useful resource on probability and statistics that would benefit anyone who works with large data sets. Taken as a whole, it is an exceptional general reference for graduate students and seasoned experimental researchers alike.' Emilie Martin-Hein, Physics Today

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.

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.

Subject Areas: The environment [RN], Earth sciences [RB], Chemistry [PN], Applied physics [PHV], Physics [PH], Astronomy, space & time [PG], Data analysis: general [GPH]