Data-analysis techniques for high-energy physics.
This is the second edition of the book published by the authors a few years ago (1990). This book brings together most of the data handling techniques in today's High Energy Physics. The book is strongly influenced by the lessons learned at proton-antiproton colliders and electron-positron colliders at CERN, Geneva and DESY, Hamburg and at other similar centres. NEWLINENEWLINENEWLINENovel approaches to triggering, for instance, the use of neural networks, are described in new examples added in Chapter 1. A gap in the first edition has been rectified by adding a section on acceptance and the computation of acceptance correction. The section on data buses has been extended by a description of the PCI bus. In Chapter 2, which is based on pattern recognition, many subsections have been added or rewritten. Extended discussions on Hough transform, neural networks and global Kalman filtering have been given. NEWLINENEWLINENEWLINEMany changes have also been made in Chapter 3. In the sections dealing with track filtering, a more compact presentation of the Kalman filter is given. The same holds for vertex filtering. NEWLINENEWLINENEWLINEChapter 4 has been extensively revised due to rapid changes in computing hardware and software. NEWLINENEWLINENEWLINEThe references to the literature have been augmented by many recent articles and books. NEWLINENEWLINENEWLINEThis a very useful book to those who are involved in High Energy Physics experiments.
scientific article; zbMATH DE number 1659885