Experimental Particle Physics is written for advanced undergraduate or beginning postgraduate students starting data analysis in experimental particle physics at the Large Hadron Collider (LHC) at CERN. Assuming only a basic knowledge of quantum mechanics and special relativity, the text reviews the current state of affairs in particle physics, before comprehensively introducing all the ingredients that go into an analysis.
Author(s): Deepak Kar
Publisher: IOP Publishing
Year: 2019
Language: English
Pages: 175
City: Bristol
PRELIMS.pdf
Preface
Author biography
Deepak Kar
CH001.pdf
Chapter 1 Groundwork
1.1 Natural units
1.2 Particle content
1.2.1 What we have
1.2.2 What we do not have
1.3 Relativistic kinematics
1.3.1 Basic ideas
1.3.2 Specific examples
Exercises
References
CH002.pdf
Chapter 2 Collisions
2.1 Effecting collisions
2.2 Measure of collisions
2.3 Coordinates
2.4 Types of collisions
Exercises
References
CH003.pdf
Chapter 3 Analysis objects
3.1 Detector objects
3.1.1 Charged particles
3.1.2 Electrons
3.1.3 Photons
3.1.4 Charged and neutral hadrons
3.1.5 Muons
3.1.6 Neutrinos/missing energy
3.2 Jets and making them
3.3 Trigger
3.4 Preparing the data
3.4.1 Trigger efficiency
3.4.2 Pile-up correction
3.4.3 Calibration
3.4.4 Isolation
3.4.5 Reconstruction and identification efficiency
3.4.6 Fakes and overlap removal
Exercises
References
CH004.pdf
Chapter 4 Theoretical view of collisions and simulating them
4.1 Theoretical overview from an experimentalist’s perspective
4.1.1 Feynman diagrams
4.1.2 Mandelstam variables
4.1.3 QCD and perturbative expansion
4.1.4 Processes at hadron colliders
4.2 Simulation programmes
4.2.1 Overview
4.2.2 Hard process and parton distribution function
4.2.3 Parton shower
4.2.4 Multiple parton interactions
4.2.5 Hadronisation
4.2.6 Electroweak corrections
4.2.7 Matching/merging
4.2.8 Tuning
4.2.9 Practicalities and software programmes
4.2.10 Detector simulation
Exercises
References
CH005.pdf
Chapter 5 Analysis
5.1 Measurements and searches
5.1.1 Measurements
5.1.2 Searches
5.2 Observables/techniques
5.2.1 Getting started
5.2.2 Cross-section
5.2.3 Minimum-bias and underlying event
5.2.4 Double parton interaction
5.2.5 Asymmetry
5.2.6 Gap fraction and azimuthal decorrelation
5.2.7 Event and jet shapes
5.2.8 Jet charge and jet pull
5.2.9 Angular observables
5.2.10 W boson reconstruction
5.2.11 Stransverse mass
5.2.12 SUSY specific observables
5.2.13 ISR tagging
5.2.14 Trigger level analysis
5.3 Analyses steps
5.3.1 Object and event selection
5.3.2 Cut optimisation
5.3.3 Reweighting
5.3.4 Signal, control and validation regions
5.3.5 Background estimation
5.3.6 Unfolding
5.3.7 Summary of analysis steps
5.4 Detour: basic statistics
5.4.1 Bayesian or frequentist?
5.4.2 Probability density function: PDFs again, but not partons
5.4.3 Common PDFs
5.4.4 Central limit theorem (CLT)
5.4.5 Likelihood and parameter estimation
5.4.6 Fit quality
5.5 ROOT terms
Exercises
References
CH006.pdf
Chapter 6 Uncertainties
6.1 Types of uncertainties
6.2 Sources of systematic uncertainties
6.3 Estimation of systematic uncertainties
6.3.1 Luminosity
6.3.2 Experimental acceptance, efficiency, calibrations
6.3.3 Background estimation
6.3.4 Unfolding
6.3.5 Theory/simulation
6.3.6 Some remarks on uncertainties
6.4 Statistical methods used in uncertainty estimation
6.4.1 Nuisance parameters
6.4.2 Profile likelihood
6.4.3 Bootstrapping
6.4.4 Combining different uncertainties
Exercises
References
CH007.pdf
Chapter 7 Presenting and interpreting the results
7.1 Constructing the plots
7.1.1 Frequency distribution
7.1.2 Correlation between variables
7.1.3 Limit plots
7.1.4 ROC curves
7.2 Interpreting the plots
7.2.1 Total cross-section
7.2.2 Charged particle distributions
7.2.3 Jet distributions
7.2.4 W/Z boson distributions
7.2.5 Top quark distributions
7.2.6 Higgs boson distributions
7.2.7 General search
7.2.8 Reinterpretation
Exercises
References
CH008.pdf
Chapter 8 Advanced topic: jet substructure
8.1 Large-radius jets
8.2 Grooming
8.3 Observables/taggers
8.4 Experimental results using jet substructure
8.5 Miscellaneous theoretical and experimental aspects
8.5.1 Ambiguity of jet identification based on origin
8.5.2 Calibration and uncertainties
8.5.3 Mass sculpting and variable radius jets
8.5.4 Lund plane
References
CH009.pdf
Chapter 9 Advanced topic: machine learning
9.1 Precursor: multivariate analyses
9.2 Machine learning in a nutshell
9.2.1 Decision trees
9.2.2 Neural networks
9.2.3 Types of neural networks
9.2.4 Types of learning
9.3 Applications in data analysis
References
APP1.pdf
Chapter
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
