TY  - BOOK
AU  - Frühwirth,Rudolf
AU  - Strandlie,Are
ED  - SpringerLink (Online service)
TI  - Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
T2  - Particle Acceleration and Detection,
SN  - 9783030657710
AV  - QC787.P3 
U1  - 539.73 23
PY  - 2021///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Particle accelerators
KW  - Measurement
KW  - Measuring instruments
KW  - Pattern recognition systems
KW  - Mathematical physics
KW  - Accelerator Physics
KW  - Measurement Science and Instrumentation
KW  - Automated Pattern Recognition
KW  - Theoretical, Mathematical and Computational Physics
N1  - Part 1. Introduction -- Chapter 1. Tracking Detectors -- Chapter 2. Event Reconstruction -- Chapter 3. Statistics and Numerical Methods -- Part 2. Track Reconstruction -- Chapter 4. Track Models -- Chapter 5. Track Finding -- Chapter 6. Track Fitting -- Part 3. Vertex Reconstruction -- Chapter 7. Vertex Finding -- Chapter 8. Vertex Fitting -- Chapter 9. Secondary Vertex Reconstruction -- Part 4. Case Studies -- Chapter 10. LHC Experiments; Open Access
N2  - 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.
UR  - https://doi.org/10.1007/978-3-030-65771-0
ER  -