Skip to main content Accessibility help
×
  • Cited by 11
    • Show more authors
    • Open Access
      You have digital access to this book
    • Select format
    • Publisher:
      Cambridge University Press
      Publication date:
      24 October 2017
      05 October 2017
      ISBN:
      9781108241922
      9781108416788
      9781009245005
      Creative Commons:
      Creative Common License - CC Creative Common License - BY Creative Common License - NC
      This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC 4.0.
      https://creativecommons.org/creativelicenses
      Dimensions:
      (246 x 189 mm)
      Weight & Pages:
      1.7kg, 716 Pages
      Dimensions:
      (246 x 189 mm)
      Weight & Pages:
      1.37kg, 720 Pages
    Open Access
    You have digital access to this book
    Selected: Digital
    View content
    Add to cart View cart Buy from Cambridge.org

    Book description

    This is a comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced students and 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 given to 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. This title is also available as open access on Cambridge Core.

    Reviews

    ‘This ambitious book provides a comprehensive, rigorous, and accessible introduction to data analysis for nuclear and particle physicists working on collider experiments, and outlines the concepts and techniques needed to carry out forefront research with modern collider data in a clear and pedagogical way. The topic of particle correlation functions, a seemingly straightforward topic with conceptual pitfalls awaiting the unaware, receives two full chapters. Professor Pruneau presents these concepts carefully and systematically, with precise definitions and extensive discussion of interpretation. These chapters should be required reading for all practitioners working in this area.'

    Peter Jacobs - Lawrence Berkeley National Laboratory

    ‘The techniques described in this textbook on correlation functions, and on efficiency and acceptance of an experimental apparatus, are key to understanding the approach used in many contemporary large-scale experiments; they are relevant for theoretical and experimental researchers alike, both in nuclear and particle physics and in many other areas where large data volumes and multi-dimensional data are investigated. I consider this an important and unique addition to the current literature on the subject.'

    Peter Braun-Munzinger - GSI Helmholtzzentrum fur Schwerionenforschung, Germany

    ‘This text is a very welcome addition to the books available in the area. It provides concise and eminently readable information on probability and statistics but also deals in quite some detail with many of the techniques used currently in running high-energy and nuclear physics experiments but not covered in standard texts. A case in point is the beautiful exposé on Kalman filtering, and the sections which deal with particle identification techniques. Presented so that theoretical researchers can get much-needed information on how data analysis works in such environments, the text is also very well suited to all students of experimental physics, and is particularly interesting for students and more senior researchers alike who have specialized in large nuclear and particle physics experiments.'

    Johanna Stachel - University of Heidelberg

    ‘Data Analysis Techniques for Physical Scientists is both monumental and accessible. While targeted towards data analysis methods in nuclear and particle physics, its breadth and depth insure that it will be of interest to a much broader audience across the physical sciences. Designed as a textbook, with ample problems and expository text, this wonderful new addition to the literature is also suitable for self-study and as a reference. As such, it is the book that I will first recommend to my students, be they undergraduates or graduate students.'

    W. A. Zajc - Columbia University, New York

    'The text is clearly written, and the book is well laid out with numerous useful illustrations. For its target audience, this is an excellent book.'

    A. H. Harker Source: Contemporary Physics

    '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 Source: Physics Today

    Refine List

    Actions for selected content:

    Select all | Deselect all
    • View selected items
    • Export citations
    • Download PDF (zip)
    • Save to Kindle
    • Save to Dropbox
    • Save to Google Drive

    Save Search

    You can save your searches here and later view and run them again in "My saved searches".

    Please provide a title, maximum of 40 characters.
    ×

    Contents

    Full book PDF
    • Frontmatter
      pp i-iv
    • Dedication
      pp v-v
    • Other
      pp vi-vi
    • Contents
      pp vii-x
    • Preface
      pp xi-xii
    • How to Read This Book
      pp xiii-xiv
    • 1 - The Scientific Method
      pp 1-14
    • Part I - Foundation in Probability and Statistics
      pp 15-16
    • 2 - Probability
      pp 17-87
    • 3 - Probability Models
      pp 88-138
    • 4 - Classical Inference I: Estimators
      pp 139-177
    • 5 - Classical Inference II: Optimization
      pp 178-226
    • 6 - Classical Inference III: Confidence Intervals and Statistical Tests
      pp 227-283
    • 7 - Bayesian Inference
      pp 284-386
    • Part II - Foundation in Probability and Statistics
      pp 387-388
    • 8 - Basic Measurements
      pp 389-459
    • 9 - Event Reconstruction
      pp 460-501
    • 10 - Correlation Functions
      pp 502-525
    • 11 - The Multiple Facets of Correlation Functions
      pp 526-576
    • 12 - Data Correction Methods
      pp 577-640
    • Part III - Simulation Techniques
      pp 641-642
    • 13 - Monte Carlo Met
      pp 643-662
    • 14 - Collision and Detector Modeling
      pp 663-686
    • References
      pp 687-697
    • Index
      pp 698-704

    Metrics

    Altmetric attention score

    Full text views

    Total number of HTML views: 0
    Total number of PDF views: 0 *
    Loading metrics...

    Book summary page views

    Total views: 0 *
    Loading metrics...

    * Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

    Usage data cannot currently be displayed.

    Accessibility standard: Unknown

    Why this information is here

    This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

    Accessibility Information

    Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.