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Search for vector-like partners of the top and bottom quarks with the ATLAS experiment / Jens Roggel. Wuppertal, August 2022
Inhalt
Introduction
The Standard Model of Particle Physics
Particles and interactions in the Standard Model
Electroweak theory
The Higgs mechanism
Quantum chromodynamics
Probing the Standard Model
Limitations of the Standard Model
Vector-like quarks
Phenomenology
Searches for vector-like quarks
Event simulation
Factorisation theorem
Hard scattering cross-section
Parton shower
Matching and merging
Hadronisation
Underlying event and pile-up
Monte Carlo event generators
ATLAS detector simulation
ATLAS Monte Carlo generator validation
Monte Carlo production
Monte Carlo validation procedure
Validation system PAVER
Validation use cases and results
Conclusion and outlook
The ATLAS experiment at the Large Hadron Collider
The Large Hadron Collider
The ATLAS experiment
Coordinate system
Inner detector
Calorimeters
Muon spectrometer
Trigger system
Luminosity measurement
Event Reconstruction
Tracks and primary vertices
Electrons
Muons
Jets
Flavour tagging
Missing transverse momentum
Overlap removal
Search for vector-like top and bottom partners
Data and simulated samples
Basic event selection
Properties of signal and background
Comparison of data and MC
Kinematic top reweighting
Signal and background separation
Neural networks
Neural networks in the search for VLQs
Background estimation
Systematic uncertainties
Handling of systematic uncertainties
Theoretical systematic uncertainties
Experimental systematic uncertainties
Statistical techniques
Profile likelihood fit
Hypothesis tests
Results
Background-only fit results
Signal-plus-background fits
Exclusion limits on signal parameters
Comparison to other VLQ searches
Outlook
Study of tt-tW interference handling schemes
Theoretical concepts
Simulated samples
Object definition and analysis selection
Comparisons in tt bulk region
Studies in the interference region
Conclusion
Conclusion
ATLAS MC validation - PAVER webpage
Search for vector-like quarks
Additional signal and background shape distributions
NN input variables in the training region
Additional distributions in the single-top CR and W+jets CR
Background-only fits
Post-fit neural network output distributions in the signal regions
Interference study
Bibliography
Acknowledgements