Pattern Recognition and Event Reconstruction in Particle Physics Experiments

Abstract

This report reviews methods of pattern recognition and event reconstruction used in modern high energy physics experiments. After a brief introduction into general concepts of particle detectors and statistical evaluation, different approaches in global and local methods of track pattern recognition are reviewed with their typical strengths and shortcomings. The emphasis is then moved to methods which estimate the particle properties from the signals which pattern recognition has associated. Finally, the global reconstruction of the event is briefly addressed.

101 pages, 58 figures

References in corpus (1)

• The STAR Time Projection Chamber: A Unique Tool for Studying High Multiplicity Events at RHIC

Cited by in corpus (12)

• Description and performance of track and primary-vertex reconstruction with the CMS tracker
• Decay Chain Fitting with a Kalman Filter
• Muon tomography imaging algorithms for nuclear threat detection inside large volume containers with the Muon Portal detector
• Track Finding at Belle II
• Transverse Spin Structure of the Nucleon through Target Single Spin Asymmetry in Semi-Inclusive Deep-Inelastic $(e,e^{\prime }{\pi }^{\pm })$ Reaction at Jefferson Lab
• An extension of the Gluckstern formulas for multiple scattering: analytic expressions for track parameter resolution using optimum weights
• Stochastic normalizing flows as non-equilibrium transformations
• Quantum speedup for track reconstruction in particle accelerators
• Interest Point Detection for Reconstruction in High Granularity Tracking Detectors
• Comparison of two hardware-based hit filtering methods for trackers in high-pileup environments
• Analytical computation of process noise matrix in Kalman filter for fitting curved tracks in magnetic field within dense, thick scatterers
• Kink Finder at Belle II