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