Data Analysis Techniques for Physical Scientists

A comprehensive guide to data analysis techniques for physical scientists, covering probability and statistics, measurements, errors, and Monte Carlo methods. It is valuable for advanced undergraduate and graduate students, as well as seasoned researchers.

Format: Paperback / softback
Length: 718 pages
Publication date: 16 June 2022
Publisher: Cambridge University Press

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.

The book is divided into four main sections:

Introduction to Probability and Statistics: This section provides an overview of the basic concepts and methods of probability and statistics, including probability distributions, random variables, and statistical inference. It covers both the frequentist and Bayesian interpretations of probability and emphasizes the importance of understanding the underlying principles and assumptions of each approach.

Measurements of Particle Production Cross-Sections, Correlation Functions, and Particle Identification: This section covers the basic concepts and techniques used for measuring particle production cross-sections, correlation functions, and particle identification. It includes discussions of particle detectors, data acquisition systems, and data analysis methods.

Statistical and Systematic Errors: This section discusses the concepts of statistical and systematic errors in data analysis. It begins with intuitive discussions of error propagation and introduces the more formal concepts of confidence intervals, credible ranges, and hypothesis testing. It also covers the methods used to estimate and correct for these errors, including the use of Monte Carlo techniques.

Unfolding or Correcting Data for Instrumental Effects: This section covers the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses. It includes discussions of data fitting techniques, such as least squares and maximum likelihood, and the use of Bayesian inference to estimate parameters and uncertainties.

In addition to these main sections, the book includes a number of appendices that provide additional information and examples on various topics related to data analysis in physical sciences. These appendices cover topics such as probability distributions, random variables, statistical inference, and Monte Carlo methods.

Overall, this book is a valuable resource for physical scientists who need to analyze data from experiments and simulations. It provides a comprehensive and up-to-date introduction to the most important data analysis techniques, and it is written in a clear and accessible style that is suitable for students and researchers at all levels.

ISBN-13: 9781009245005