Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors (Particle Acceleration and Detection) - Hardcover

Frühwirth, Rudolf; Strandlie, Are

Hardcover

ISBN 10: 3030657701 ISBN 13: 9783030657703

Publisher: Springer, 2021

Synopsis

This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments.    

About this title

About the Author

Dr Rudolf Frühwirth is retired from a senior staff position at the Institute of High Energy Physics of the Austrian Academy of Sciences in Vienna, where he headed the Algorithm and Software Development group until end of 2017. He studied mathematics at the TU Wien, from which he received his Diploma degree in 1976 and his Doctor of Technical Sciences degree in 1986. From 1979 to 1984 he was Research Associate at CERN. Since 1996 he is Dozent (Reader) in Statistical Data Analysis at TU Wien, where he regularly gives lectures on statistics and data analysis to physicists. He has contributed to the reconstruction software of numerous experiments, among them WA6, EHS, UA1, DELPHI and CMS at CERN, as well as Belle II at KEK. His research interests are data reconciliation with nonnormal data, pattern recognition in particle detectors, and statistical methods in track and vertex reconstruction, with the focus on adaptive and robust algorithms.

Professor Are Strandlie, currently full professor of physics at NTNU - Norwegian University of Science and Technology, received his Master of Science degree in Theoretical Physics in 1995 and his Doctor of Science degree in Experimental Particle Physics in 2000, both from the University of Oslo. He was a Research Fellow at CERN between 2001 and 2003, where he was working on track reconstruction software development for the CMS Tracker. He has held a position as Adjunct Professor at the Department of Physics, University of Oslo, giving lectures about statistics and data analysis techniques in experimental high-energy physics. He is now involved in the ATLAS experiment at CERN. Strandlie's research interests are concentrated around various aspects of the analysis of high-energy physics data, including the development and application of adaptive methods for track reconstruction.

From the Back Cover

This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments. 

Edition details

• Publisher: Springer
• Publication date: 2021
• Language: English
• ISBN 10: 3030657701
• ISBN 13: 9783030657703
• Binding: Hardcover
• Edition number: 1
• Number of pages: 220