Description

Up-dated indispensable guide to handling and analysing data obtained from high-energy and nuclear physics experiments. Now thoroughly revised and up-dated, this indispensable guide describes techniques for handling and analysing large and complex data samples obtained from high-energy and nuclear physics experiments. It includes pattern recognition techniques to group measurements into physically meaningful objects like particle tracks and methods of extracting maximum information from available measurements. Now thoroughly revised and up-dated, this indispensable guide describes techniques for handling and analysing large and complex data samples obtained from high-energy and nuclear physics experiments. It includes pattern recognition techniques to group measurements into physically meaningful objects like particle tracks and methods of extracting maximum information from available measurements. 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. Preface; Abbreviations; Symbols; Intoduction; 1. Real-time data triggering and filtering; 2. Pattern recognition; 3. Track and vertex fitting; 4. Tools and concepts for data analysis; References; Index.