Keywords:
Particles (Nuclear physics) -- Experiments -- Data processing.
;
Nuclear physics -- Experiments -- Data processing.
;
Electronic books.
Description / Table of Contents:
Now thoroughly revised and up-dated, this indispensable guide describes techniques for handling and analysing large and complex data samples obtained from high-energy and nuclear physics experiments. It includes pattern recognition techniques to group measurements into physically meaningful objects like particle tracks and methods of extracting maximum information from available measurements.
Type of Medium:
Online Resource
Pages:
1 online resource (414 pages)
Edition:
2nd ed.
ISBN:
9781139141307
Series Statement:
Cambridge Monographs on Particle Physics, Nuclear Physics and Cosmology Series ; v.Series Number 11
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=802918
DDC:
539.7/6
Language:
English
Note:
Cover -- Data Analysis Techniques for High-Energy Physics -- CAMBRIDGE MONOGRAPHS ON PARTICLE PHYSICS, NUCLEAR PHYSICS AND COSMOLOGY -- Title -- Copyright -- Contents -- Preface to the second edition -- Preface to the first edition -- Abbreviations -- Symbols -- Introduction -- 1. Real-time data triggering and filtering -- 1.1 Definitions and goals of triggers and filters -- 1.1.1 General properties of particle accelerators -- 1.1.2 Secondary beams -- 1.1.3 Energy balance in scattering experiments -- 1.1.4 Luminosity -- 1.1.5 Time structure of accelerators -- 1.1.5.1 Time structure at fixed-target accelerators. -- 1.1.5.2 Time structure at colliders. -- 1.1.6 Event rates at different accelerators -- 1.1.6.1 Event rates at fixed-target accelerators. -- 1.1.6.2 Event rates in pp or pp colliders. -- 1.1.6.3 Event rates in e+ e− colliders. -- 1.1.6.4 Event rates at electron-proton colliders. -- 1.1.7 Background rates -- 1.2 Trigger schemes -- 1.2.1 On-line data reduction -- 1.2.2 Dead time of electronic components -- 1.2.3 True and wrong coincidences, accidentals -- 1.2.4 Multi-level triggers -- 1.3 Queuing theory, queuing simulation and reliability -- 1.3.1 Queuing theory -- 1.3.2 Queuing simulation -- 1.3.3 Reliability theory -- 1.4 Classifications of triggers -- 1.4.1 Trigger on event topology -- 1.4.1.1 Trigger on track multiplicity. -- 1.4.1.2 Trigger on direction of particles. -- 1.4.1.3 Trigger on momentum of particles. -- 1.4.1.4 Trigger on coplanarity. -- 1.4.2 Trigger on type of particle -- 1.4.2.1 Photons and Neutral Pions, γ and π0. -- 1.4.2.2 Electrons, e. -- 1.4.2.3 Muons, μ. -- 1.4.2.4 Charged pions, π. -- 1.4.2.5 Kaons, K. -- 1.4.2.6 Protons, p. -- 1.4.3 Trigger on deposited energy -- 1.4.4 Trigger on missing energy -- 1.4.5 Trigger on invariant mass -- 1.4.6 Trigger on interaction point (vertex) -- 1.4.7 Acceptance.
,
1.5 Examples of triggers -- 1.5.1 Fixed-flow triggers -- 1.5.1.1 A simple fixed-flow trigger to measure angular distributions. -- 1.5.1.2 A fixed-flow trigger to find curved tracks. -- 1.5.1.3 A simple arithmetic trigger to measure total cross section. -- 1.5.2 Track finding with a lumped delay line -- 1.5.3 Track finding with memory look-up tables -- 1.5.4 Trigger on tracks with field-programmable arrays -- 1.5.5 Track finders in the trigger with variable-flow data-driven processors -- 1.5.6 A microprogrammed track processor with CAM and look-up tables -- 1.5.7 Examples of triggers on energy -- 1.5.7.1 Example of analog trigger in the ASP experiment. -- 1.5.7.2 Example of energy trigger in the ZEUS calorimeter. -- 1.5.8 A data-driven trigger on invariant mass -- 1.5.9 Triggering on neutral pions with neural networks -- 1.5.1 0 Examples of triggers on interaction point -- 1.5.10.1 Trigger on charge division. -- 1.5.11 A trigger on interaction point for short-lived particles with a microstrip detector -- 1.6 Implementation of triggers -- 1. 6.1 Electronic components -- 1.6.1.1 Pulse formers, discriminators. -- 1.6.1.2 Window discriminators. -- 1.6.1.3 Look-up tables. -- 1.6.1.4 FPLAs. -- 1.6.1.5 PALs. -- 1.6.1.6 CAMs. -- 1.6.2 A chip for neural networks -- 1.6.3 Pipelines -- 1.6.3.1 Delay lines. -- 1.6.3.2 AMU (Analog-Memory Unit). -- 1.6.3.3 Digital pipeline. -- 1.7 Multiprogramming -- 1.7.1 Digital Signal Processors (DSPs) -- 1.7.2 Parallel processing -- 1.7.2.1 Needs for computing power and parallel processing. -- 1.7.2.2 Classification of parallel processing. -- 1.7.2.3 Pipelining for execution. -- 1.7.2.4 Multiple functional units. -- 1.7.2.5 Many-processor systems. -- 1.7.2.6 Programming aspects for parallel processing. -- 1.8 Communication lines, bus systems -- 1.8.1 Synchronous and asynchronous buses -- 1.8.2 Addressing -- 1.8.3 Data transfers.
,
1.8.4 Control lines -- 1.8.5 Responses -- 1.8.6 Interrupts -- 1.8.7 Multiple masters, bus arbitration -- 1.8.8 Characteristics of buses used in physics experiments -- 1.8.8.1 Characteristics of CAMAC -- 1.8.8.2 Characteristics of FASTBUS -- 1.8.8.3 Characteristics of VM E -- 1.8.8.4 Characteristics of PCl -- 1.8.9 Standardization of data buses -- 2. Pattern recognition -- 2.1 Foundations of track finding -- 2.1.1 Track detectors -- 2.1.1.1 Gaseous detectors. -- 2.1.1.2 Solid-state detectors. -- 2.1.1.3 Tracking systems. -- 2.1.2 Some techniques of track modelling -- 2.1.2.1 Circles, polynomials, and splines for curve approximation. -- 2.1.2.2 Interpolation and extrapolation. -- 2.1.2.3 Parametrization. -- 2.2 Principles of pattern recognition -- 2.2.1 Pattern space -- 2.2.2 Training sample and covariance matrix -- 2.2.3 Object classification -- 2.2.4 Feature space -- 2.2.5 Classes, prototypes, and metric -- 2.2.6 Template matching -- 2.2.7 Linear feature extraction -- 2.2.8 Minimum Spanning Tree (MST) -- 2.2.9 Combinatorial optimization -- 2.3 Basic aspects of track finding -- 2.3.1 Point removal -- 2.3.2 Track quality -- 2.3.3 Working in projections or in space -- 2.3.4 Treating track overlaps -- 2.3.5 Compatibility of track candidates -- 2.3.6 Efficiency of track finding -- 2.4 Methods of track finding -- 2.4.1 A classification -- 2.4.2 Local methods -- 2.4.2.1 Track following. -- 2.4.2.2 Kalman filter. -- 2.4.2.3 Track roads. -- 2.4.2.4 Track elements. -- 2.4.3 Global methods -- 2.4.3.1 The combinatorial method. -- 2.4.3.2 Global Kalman filter. -- 2.4.3.3 The histogramming method. -- 2.4.3.4 The Hough transform. -- 2.4.3.5 Template matching. -- 2.4.3.6 Minimum Spanning Tree. -- 2.4.3.7 Hopjield networks. -- 2.4.3.8 Deformable templates, elastic arms. -- 2.5 Finding of particle showers -- 2.5.1 Some definitions -- 2.5.1.1 Total absorption.
,
2.5.1.2 Calorimeter properties. -- 2.5.1.3 Shower properties. -- 2.5.1.4 Calorimeter applications. -- 2.5.2 Physical processes in calorimeters -- 2.5.2.1 Electromagnetic showers. -- 2.5.2.2 Hadronic showers. -- 2.5.3 Calorimeter parameters -- 2.5.3.1 Homogeneous or sampling calorimeters. -- 2.5.3.2 Granularity. -- 2.5.3.3 Energy resolution. -- 2.5.3.4 Containment. -- 2.5.3.5 Hermeticity. -- 2.5.3.6 Compensation. -- 2.5.3.7 Backscattering. -- 2.5.3.8 Calibration and monitoring. -- 2.5.4 Shower parameters -- 2.5.4.1 Longitudinal shower shape -- 2.5.4.2 Lateral shower shape -- 2.5.5 Shower simulation -- 2.5.6 Examples of calorimeter algorithms -- 2.5.6.1 Global flow of energy and missing energy. -- 2.5.6.2 Photon and π0 reconstruction in the Crystal Barrel experiment. -- 2.5.6.3 e/π separation in the Mark III electromagnetic shower counter. -- 2.5.6.4 Jet finding as part of the top quark search in the D0 experiment. -- 2.5.6.5 π0 reconstruction in NA48. -- 2.6 Identifying particles in ring-imaging Cherenkov counters -- 2.6.1 The RICH technique -- 2.6.2 Examples for analysis using RICH detectors -- 2.6.2.1 Particle identification in DELPHI and SLD. -- 2.6.2.2 Fast two-photon identification in CERES and HADES. -- 2.6.2.3 Particle identification in BABAR. -- 3. Track and vertex fitting -- 3.1 The task of track fitting -- 3.2 Estimation of track parameters -- 3.2.1 Basic concepts -- 3.2.2 Global track fitting by the Least Squares Method (LSM) -- 3.2.3 A few remarks on estimation theory -- 3.2.3.1 Generalities. -- 3.2.3.2 The LSM in practice. -- 3.2.3.3 The χ2 distribution. -- 3.2.4 Test for goodness of fit -- 3.2.5 Recursive track fitting by the LSM (the Kalman filter) -- 3.2.6 Robust filtering -- 3.3 Fitting the tracks of charged particles -- 3.3.1 The track model -- 3.3.1.1 The equations of motion. -- 3.3.1.2 The choice of track parameters.
,
3.3.1.3 Several types of track models. -- 3.3.1.4 The field representation. -- 3.3.1.5 The effects of matter on the trajectory. -- 3.3.1.6 The Landau distribution. -- 3.3.2 The weight matrix -- 3.3.2.1 The measurement error of a detector. -- 3.3.2.2 Weight matrix formalism for multiple scattering. -- 3.3.2.3 Resolution of magnet spectrometer. -- 3.3.3 Track element merging -- 3.3.4 Numerical minimization technique -- 3.4 Association of tracks to vertices -- 3.4.1 Basic concepts -- 3.4.2 Global vertex fit and Kalman filter -- 3.4.3 Track association and robust vertex fitting -- 3.4.4 Kinematical constraints -- 3.5 Track reconstruction: examples and final remarks -- 4. Tools and concepts for data analysis -- 4.1 Abstracting formulae and data in the computer -- 4.2 Data access methods -- 4.3 Graphics -- 4.4 Multidimensional analysis -- 4.5 Data selection -- 4.5.1 A simple fictitious example -- 4.5.2 An example from an experiment -- 4.5.3 Practical conclusion -- 4.6 Data accumulation, projection, and presentation -- 4.6.1 Binning -- 4.6.2 Error analysis -- 4.6.3 Presentation -- References -- Index.
Permalink
