Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors.
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Superior document: | Particle Acceleration and Detection Series |
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TeilnehmendeR: | |
Place / Publishing House: | Cham : : Springer International Publishing AG,, 2021. {copy}2021. |
Year of Publication: | 2021 |
Edition: | 1st ed. |
Language: | English |
Series: | Particle Acceleration and Detection Series
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Online Access: | |
Physical Description: | 1 online resource (208 pages) |
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Table of Contents:
- Intro
- Preface
- Scope
- Content
- Audience
- Acknowledgements
- A Note on the References
- Typesetting and Notation
- Contents
- List of Figures
- List of Tables
- Part I Introduction
- 1 Tracking Detectors
- 1.1 Introduction
- 1.2 Gaseous Tracking Detectors
- 1.2.1 Multi-wire Proportional Chamber
- 1.2.2 Planar Drift Chamber
- 1.2.3 Cylindrical Drift Chamber
- 1.2.4 Drift Tubes
- 1.2.5 Time Projection Chamber
- 1.2.6 Micro-pattern Gas Detectors
- 1.3 Semiconductor Tracking Detectors
- 1.3.1 Silicon Strip Sensors
- 1.3.2 Hybrid Pixel Sensors
- 1.3.3 Silicon Drift Sensors
- 1.4 Scintillating Fiber Trackers
- 1.5 Alignment
- 1.6 Tracking Systems
- 1.6.1 Detectors at the LHC
- 1.6.1.1 ALICE
- 1.6.1.2 ATLAS
- 1.6.1.3 CMS
- 1.6.1.4 LHCb
- 1.6.2 Belle II and CBM
- 1.6.2.1 Belle II
- 1.6.2.2 CBM
- References
- 2 Event Reconstruction
- 2.1 Trigger and Data Acquisition
- 2.1.1 General Remarks
- 2.1.2 The CMS Trigger System
- 2.1.3 The LHCb Trigger System
- 2.2 Track Reconstruction
- 2.3 Vertex Reconstruction
- 2.4 Physics Objects Reconstruction
- 2.4.1 Particle ID by Dedicated Detectors
- 2.4.2 Particle and Object ID by Tracking and Calorimetry
- References
- 3 Statistics and Numerical Methods
- 3.1 Function Minimization
- 3.1.1 Newton-Raphson Method
- 3.1.2 Descent Methods
- 3.1.2.1 Line Search
- 3.1.2.2 Steepest Descent
- 3.1.2.3 Quasi-Newton Methods
- 3.1.2.4 Conjugate Gradients
- 3.1.3 Gradient-Free Methods
- 3.2 Statistical Models and Estimation
- 3.2.1 Linear Regression Models
- 3.2.2 Nonlinear Regression Models
- 3.2.3 State Space Models
- 3.2.3.1 Linear State Space Models and the Kalman Filter
- 3.2.3.2 Nonlinear State Space Models and the Extended Kalman Filter
- 3.3 Clustering
- 3.3.1 Hierarchical Clustering
- 3.3.2 Partitional Clustering
- 3.3.3 Model-Based Clustering.
- References
- Part II Track Reconstruction
- 4 Track Models
- 4.1 The Equations of Motion
- 4.2 Track Parametrization
- 4.3 Track Propagation
- 4.3.1 Homogeneous Magnetic Fields
- 4.3.2 Inhomogeneous Magnetic Fields
- 4.3.2.1 Runge-Kutta Methods
- 4.3.2.2 Approximate Analytical Formula
- 4.4 Error Propagation
- 4.4.1 Homogeneous Magnetic Fields
- 4.4.1.1 Transformation from One Curvilinear Frame to Another
- 4.4.1.2 Transformations Between Curvilinear and Local Frames at a Fixed Point on the Particle Trajectory
- 4.4.1.3 Transformations Between Global Cartesian and Local Frames
- 4.4.2 Inhomogeneous Magnetic Fields
- 4.5 Material Effects
- 4.5.1 Multiple Scattering
- 4.5.1.1 The Distribution of the Scattering Angle
- 4.5.1.2 Multiple Scattering in Track Propagation
- 4.5.2 Energy Loss by Ionization
- 4.5.2.1 Mean Energy Loss
- 4.5.2.2 Ionization Energy Loss in Track Propagation
- 4.5.3 Energy Loss by Bremsstrahlung
- 4.5.3.1 Mean and Distribution of the Energy Loss
- 4.5.3.2 Approximation by Gaussian Mixtures
- References
- 5 Track Finding
- 5.1 Basic Techniques
- 5.1.1 Conformal Transformation
- 5.1.2 Hough Transform
- 5.1.3 Artificial Retina
- 5.1.4 Legendre Transform
- 5.1.5 Cellular Automaton
- 5.1.6 Neural Networks
- 5.1.6.1 Hopfield Network
- 5.1.6.2 Recurrent Neural Network
- 5.1.6.3 Graph Neural Network
- 5.1.7 Track Following and the Combinatorial Kalman Filter
- 5.1.8 Pattern Matching
- 5.2 Online Track Finding
- 5.2.1 CDF Vertex Trigger
- 5.2.2 ATLAS Fast Tracker
- 5.2.3 CMS Track Trigger
- 5.2.3.1 Time Multiplexing
- 5.2.3.2 Pattern Matching
- 5.3 Candidate Selection
- References
- 6 Track Fitting
- 6.1 Least-Squares Fitting
- 6.1.1 Least-Squares Regression
- 6.1.2 Extended Kalman Filter
- 6.1.3 Regression with Breakpoints
- 6.1.4 General Broken Lines
- 6.1.5 Triplet Fit.
- 6.1.6 Fast Track Fit by Affine Transformation
- 6.2 Robust and Adaptive Fitting
- 6.2.1 Robust Regression
- 6.2.2 Deterministic Annealing Filter
- 6.2.3 Gaussian-Sum Filter
- 6.3 Linear Approaches to Circle and Helix Fitting
- 6.3.1 Conformal Mapping Method
- 6.3.2 Chernov and Ososkov's Method
- 6.3.3 Karimäki's Method
- 6.3.4 Riemann Fit
- 6.3.5 Helix Fitting
- 6.4 Track Quality
- 6.4.1 Testing the Track Hypothesis
- 6.4.2 Detection of Outliers
- 6.4.3 Kink Finding
- References
- Part III Vertex Reconstruction
- 7 Vertex Finding
- 7.1 Introduction
- 7.2 Primary Vertex Finding in 1D
- 7.2.1 Divisive Clustering
- 7.2.2 Model-Based Clustering
- 7.2.3 EM Algorithm with Deterministic Annealing
- 7.2.4 Clustering by Deterministic Annealing
- 7.3 Primary Vertex Finding in 3D
- 7.3.1 Preclustering
- 7.3.2 Greedy Clustering
- 7.3.3 Iterated Estimators
- 7.3.4 Topological Vertex Finder
- 7.3.5 Medical Imaging Vertexer
- References
- 8 Vertex Fitting
- 8.1 Least-Squares Fitting
- 8.1.1 Straight Tracks
- 8.1.1.1 Exact Fit
- 8.1.1.2 Simplified Fit
- 8.1.2 Curved Tracks
- 8.1.2.1 Nonlinear Regression
- 8.1.2.2 Extended Kalman Filter
- 8.1.2.3 Fit with Perigee Parameters
- 8.2 Robust and Adaptive Vertex Fitting
- 8.2.1 Vertex Fit with M-Estimator
- 8.2.2 Adaptive Vertex Fit with Annealing
- 8.2.3 Vertex Quality
- 8.3 Kinematic Fit
- References
- 9 Secondary Vertex Reconstruction
- 9.1 Introduction
- 9.2 Decays of Short-Lived Particles
- 9.3 Decays of Long-Lived Particles
- 9.4 Photon Conversions
- 9.5 Hadronic Interactions
- References
- Part IV Case Studies
- 10 LHC Experiments
- 10.1 ALICE
- 10.2 ATLAS
- 10.3 CMS
- 10.4 LHCb
- References
- 11 Belle II and CBM
- 11.1 Belle II
- 11.2 CBM
- References
- A Jacobians of the Parameter Transformations
- Transformation from One Curvilinear Frame to Another.
- Transformations Between a Local Frame and the Curvilinear Frame
- Transformations Between the Intermediate Cartesian Frame and the Local Frame
- B Regularization of the Kinematic Fit
- Reference
- C Software
- FairRoot
- ACTS: A Common Tracking Software
- GBL: General Broken Lines
- GENFIT
- RAVE
- References
- Glossary and Abbreviations
- Index.