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Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
Rudolf Frühwirth and Are Strandlie
2021
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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 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.
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Keywords
- Accelerator Physics
- artificial intelligence
- Automated Pattern Recognition
- Calolimator for pattern recognition
- Clustering algorithms
- Computer science
- Computing & information technology
- Event reconstruction
- Experimental High-Energy Physics
- LHC
- Mathematical physics
- Mathematics & science
- Measurement Science and Instrumentation
- Mensuration & systems of measurement
- Numerical and Computational Physics, Simulation
- open access
- Particle & high-energy physics
- Particle Acceleration and Detection, Beam Physics
- pattern recognition
- Physics
- Science: general issues
- Scientific standards
- Scientific standards, measurement etc
- Theoretical, Mathematical and Computational Physics
- Tracking detectors in High Energy Physics
- Triggering event and data analysis
- Vertex of particle collision
- Vertex reconstruction
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