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E-raamat: Data Analysis Techniques for High-Energy Physics

(Deutsches Elektronen-Sychrotron (DESY) Hamburg), (Austrian Academy of Sciences), (Conseil Européen de Recherches Nucléaires, Geneva), (Conseil Européen de Recherches Nucléaires, Geneva), (Austrian Academy of Sciences)
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Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments. The observation of particle interactions involves the analysis of large and complex data samples. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a sometimes huge background. The use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects like particle tracks or showers is then examined and the track and vertex fitting methods necessary to extract the maximum amount of information from the available measurements are explained. The final chapter describes tools and methods which are useful to the experimenter in the physical interpretation and in the presentation of the results. This indispensable guide will appeal to graduate students, researchers and computer and electronic engineers involved with experimental physics.

Muu info

Up-dated indispensable guide to handling and analysing data obtained from high-energy and nuclear physics experiments.
Preface to the second edition xi
Preface to the first edition xiv
Abbreviations xvi
Symbols xviii
Introduction xix
Real-time data triggering and filtering
1(109)
Definitions and goals of triggers and filters
1(9)
General properties of particle accelerators
1(1)
Secondary beams
2(1)
Energy balance in scattering experiments
3(2)
Luminosity
5(1)
Time structure of accelerators
6(1)
Event rates at different accelerators
7(2)
Background rates
9(1)
Trigger schemes
10(8)
On-line data reduction
10(2)
Dead time of electronic components
12(3)
True and wrong coincidences, accidentals
15(1)
Multi-level triggers
15(3)
Queuing theory, queuing simulation and reliability
18(18)
Queuing theory
18(10)
Queuing simulation
28(2)
Reliability theory
30(6)
Classifications of triggers
36(13)
Trigger on event topology
38(1)
Trigger on type of particle
39(3)
Trigger on deposited energy
42(1)
Trigger on missing energy
43(1)
Trigger on invariant mass
43(1)
Trigger on interaction point (vertex)
43(1)
Acceptance
44(5)
Examples of triggers
49(26)
Fixed-flow triggers
49(3)
Track finding with a lumped delay line
52(1)
Track finding with memory look-up tables
52(3)
Trigger on tracks with field-programmable arrays
55(2)
Track finders in the trigger with variable-flow data-driven processors
57(3)
A microprogrammed track processor with CAM and look-up tables
60(2)
Examples of triggers on energy
62(4)
A data-driven trigger on invariant mass
66(3)
Triggering on neutral pions with neural networks
69(3)
Examples of triggers on interaction point
72(1)
A trigger on interaction point for short-lived particles with a microstrip detector
73(2)
Implementation of triggers
75(11)
Electronic components
75(7)
A chip for neural networks
82(1)
Pipelines
83(3)
Multiprogramming
86(8)
Digital Signal Processors (DSPs)
86(1)
Parallel processing
87(7)
Communication lines, bus systems
94(16)
Synchronous and asynchronous buses
97(1)
Addressing
98(2)
Data transfers
100(1)
Control lines
100(1)
Responses
100(1)
Interrupts
101(2)
Multiple masters, bus arbitration
103(1)
Characteristics of buses used in physics experiments
104(4)
Standardization of data buses
108(2)
Pattern recognition
110(111)
Foundations of track finding
110(20)
Track detectors
111(11)
Some techniques of track modelling
122(8)
Principles of pattern recognition
130(19)
Pattern space
130(1)
Training sample and covariance matrix
131(2)
Object classification
133(1)
Feature space
133(3)
Classes, prototypes, and metric
136(2)
Template matching
138(2)
Linear feature extraction
140(4)
Minimum Spanning Tree (MST)
144(2)
Combinatorial optimization
146(3)
Basic aspects of track finding
149(14)
Point removal
151(1)
Track quality
152(1)
Working in projections or in space
153(3)
Treating track overlaps
156(1)
Compatibility of track candidates
157(3)
Efficiency of track finding
160(3)
Methods of track finding
163(17)
A classification
163(1)
Local methods
163(6)
Global methods
169(11)
Finding of particle showers
180(32)
Some definitions
180(5)
Physical processes in calorimeters
185(3)
Calorimeter parameters
188(6)
Shower parameters
194(5)
Shower simulation
199(2)
Examples of calorimeter algorithms
201(11)
Identifying particles in ring-imaging Cherenkov counters
212(9)
The RICH technique
212(3)
Examples for analysis using RICH detectors
215(6)
Track and vertex fitting
221(112)
The task of track fitting
221(3)
Some symbols used in this chapter
224(1)
Estimation of track parameters
224(33)
Basic concepts
224(2)
Global track fitting by the Least Squares Method (LSM)
226(4)
A few remarks on estimation theory
230(9)
Test for goodness of fit
239(5)
Recursive track fitting by the LSM (the Kalman filter)
244(8)
Robust filtering
252(5)
Fitting the tracks of charged particles
257(57)
The track model
257(37)
The weight matrix
294(15)
Track element merging
309(3)
Numerical minimization technique
312(2)
Association of tracks to vertices
314(11)
Basic concepts
314(3)
Global vertex fit and Kalman filter
317(3)
Track association and robust vertex fitting
320(2)
Kinematical constraints
322(3)
Track reconstruction: examples and final remarks
325(8)
Tools and concepts for data analysis
333(25)
Abstracting formulae and data in the computer
334(3)
Data access methods
337(2)
Graphics
339(5)
Multidimensional analysis
344(2)
Data selection
346(7)
A simple fictitious example
348(3)
An example from an experiment
351(1)
Practical conclusion
352(1)
Data accumulation, projection, and presentation
353(5)
Binning
354(1)
Error analysis
355(1)
Presentation
355(3)
References 358(17)
Index 375