Data Analysis Techniques for High-Energy Physics

by R. Frühwirth , M. Regler , R. K. Bock , H. Grote , D. Notz

• ISBN13: 9780521635486
• ISBN10: 0521635489
• Edition: 2nd
• Format: Paperback
• Copyright: 2000-08-28
• Publisher: Cambridge University Press

Summary

Now thoroughly revised and up-dated, this volume describes techniques for handling and analyzing data obtained from high-energy and nuclear physics experiments. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a large background. The use of pattern recognition techniques to group the huge number of measurements into physically meaningful objects such as 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 useful for the physical interpretation and presentation of results.

Table of Contents

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