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Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors

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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 fin...

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Bibliographic Details
Main Author: Frühwirth, Rudolf (auth)
Other Authors: Strandlie, Are (auth)
Format: Electronic Book Chapter
Language:English
Published: Springer Nature 2021
Series:Particle Acceleration and Detection
Subjects:
Particle & high-energy physics
Mensuration & systems of measurement
Pattern recognition
Mathematical physics
Particle Acceleration and Detection, Beam Physics
Measurement Science and Instrumentation
Pattern Recognition
Numerical and Computational Physics, Simulation
Accelerator Physics
Automated Pattern Recognition
Theoretical, Mathematical and Computational Physics
Event reconstruction
Tracking detectors in High Energy Physics
Vertex reconstruction
Clustering algorithms
Experimental High-Energy Physics
LHC
Calolimator for pattern recognition
Vertex of particle collision
Triggering event and data analysis
Open access
Scientific standards, measurement etc
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Description
Summary: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.
Physical Description:1 electronic resource (203 p.)
ISBN:978-3-030-65771-0
9783030657710
Access:Open Access

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