Track and vertex reconstruction: From classical to adaptive methods
- Gjoevik University College, P.O. Box 191, N-2802 Gjoevik (Norway)
This paper reviews classical and adaptive methods of track and vertex reconstruction in particle physics experiments. Adaptive methods have been developed to meet the experimental challenges at high-energy colliders, in particular, the CERN Large Hadron Collider. They can be characterized by the obliteration of the traditional boundaries between pattern recognition and statistical estimation, by the competition between different hypotheses about what constitutes a track or a vertex, and by a high level of flexibility and robustness achieved with a minimum of assumptions about the data. The theoretical background of some of the adaptive methods is described, and it is shown that there is a close connection between the two main branches of adaptive methods: neural networks and deformable templates, on the one hand, and robust stochastic filters with annealing, on the other hand. As both classical and adaptive methods of track and vertex reconstruction presuppose precise knowledge of the positions of the sensitive detector elements, the paper includes an overview of detector alignment methods and a survey of the alignment strategies employed by past and current experiments.
- OSTI ID:
- 22038498
- Journal Information:
- Reviews of Modern Physics, Vol. 82, Issue 2; Other Information: (c) 2010 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6861
- Country of Publication:
- United States
- Language:
- English
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