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231204s2021 xx o ||||0 eng d |
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|a 9783030657710
|q (electronic bk.)
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|z 9783030657703
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|a (MiAaPQ)EBC6509884
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|a (Au-PeEL)EBL6509884
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|a (OCoLC)1240210729
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|a MiAaPQ
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|c MiAaPQ
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|a QC787.P3
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|a ühwirth, Rudolf.
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|a Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors.
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|a 1st ed.
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|a Cham :
|b Springer International Publishing AG,
|c 2021.
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|c {copy}2021.
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|a 1 online resource (208 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|2 rdacarrier
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|a Particle Acceleration and Detection Series
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|a 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.
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|a 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.
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|a 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.
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|a 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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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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|a Strandlie, Are.
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|i Print version:
|a ühwirth, Rudolf
|t Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
|d Cham : Springer International Publishing AG,c2021
|z 9783030657703
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| 797 |
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|a ProQuest (Firm)
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| 830 |
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|a Particle Acceleration and Detection Series
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6509884
|z Click to View
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