A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.
A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.
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.
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.
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