Data Analysis Techniques for High-Energy Physics
eBook - PDF

Data Analysis Techniques for High-Energy Physics

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Data Analysis Techniques for High-Energy Physics

Book details
Table of contents
Citations

About This Book

Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments. The observation of particle interactions involves the analysis of large and complex data samples. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a sometimes huge background. The use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects like particle tracks or showers is then examined and the track and vertex fitting methods necessary to extract the maximum amount of information from the available measurements are explained. The final chapter describes tools and methods which are useful to the experimenter in the physical interpretation and in the presentation of the results. This indispensable guide will appeal to graduate students, researchers and computer and electronic engineers involved with experimental physics.

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Yes, you can access Data Analysis Techniques for High-Energy Physics by R. Frühwirth,M. Regler,R. K. Bock,H. Grote,D. Notz in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Nuclear Physics. We have over one million books available in our catalogue for you to explore.

Information

Year
2000
ISBN
9781139142199

Table of contents

  1. Cover
  2. Data Analysis Techniques for High-Energy Physics
  3. CAMBRIDGE MONOGRAPHS ON PARTICLE PHYSICS, NUCLEAR PHYSICS AND COSMOLOGY
  4. Title
  5. Copyright
  6. Contents
  7. Preface to the second edition
  8. Preface to the first edition
  9. Abbreviations
  10. Symbols
  11. Introduction
  12. 1. Real-time data triggering and filtering
  13. 2. Pattern recognition
  14. 3. Track and vertex fitting
  15. 4. Tools and concepts for data analysis
  16. References
  17. Index