Data Analysis Techniques for High-Energy Physics

Now thoroughly revised and up-dated, this volume describes techniques for handling and analyzing data obtained from high-energy and nuclear physics experiments. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a large background. The use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects such as 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 useful for the physical interpretation and presentation of results.

Now thoroughly revised and up-dated, this book describes techniques for handling and analysing the large amount of complex data obtained from high-energy and nuclear physics experiments. It discusses the use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects and explains methods necessary to extract the maximum amount of information from the available measurements. This indispensable guide will appeal to graduate students, researchers and computer and electronic engineers involved with experimental physics.