Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors.

Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors.

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Bibliographic Details
Superior document:Particle Acceleration and Detection Series
:
ühwirth, Rudolf.
TeilnehmendeR:
Strandlie, Are.
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
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.

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