• PhD Thesis - Pablo de Castro
  • Abstract
  • Preface
  • Acknowledgements
  • Introduction
  • 1 Theory of Fundamental Interactions
    • 1.1 The Standard Model
      • 1.1.1 Essentials of Quantum Field Theory
      • 1.1.2 Quantum Chromodynamics
      • 1.1.3 Electroweak Interactions
      • 1.1.4 Symmetry Breaking and the Higgs Boson
    • 1.2 Beyond the Standard Model
      • 1.2.1 Known Limitations
      • 1.2.2 Possible Extensions
    • 1.3 Phenomenology of Proton Collisions
      • 1.3.1 Main Observables
      • 1.3.2 Parton Distribution Functions
      • 1.3.3 Factorisation and Generation of Hard Processes
      • 1.3.4 Hadronization and Parton Showers
  • 2 Experiments at Particle Colliders
    • 2.1 The Large Hadron Collider
      • 2.1.1 Injection and Acceleration Chain
      • 2.1.2 Operation Parameters
      • 2.1.3 Multiple Hadron Interactions
      • 2.1.4 Experiments
    • 2.2 The Compact Muon Solenoid
      • 2.2.1 Experimental Geometry
      • 2.2.2 Magnet
      • 2.2.3 Tracking System
      • 2.2.4 Electromagnetic Calorimeter
      • 2.2.5 Hadronic Calorimeter
      • 2.2.6 Muon System
      • 2.2.7 Trigger and Data Acquisition
    • 2.3 Event Simulation and Reconstruction
      • 2.3.1 A Generative View
      • 2.3.2 Detector Simulation
      • 2.3.3 Event Reconstruction
  • 3 Statistical Modelling and Inference at the LHC
    • 3.1 Statistical Modelling
      • 3.1.1 Overview
      • 3.1.2 Simulation as Generative Modelling
      • 3.1.3 Dimensionality Reduction
      • 3.1.4 Known Unknowns
    • 3.2 Statistical Inference
      • 3.2.1 Likelihood-Free Inference
      • 3.2.2 Hypothesis Testing
      • 3.2.3 Parameter Estimation
  • 4 Machine Learning in High-Energy Physics
    • 4.1 Problem Description
      • 4.1.1 Probabilistic Classification and Regression
    • 4.2 Machine Learning Techniques
      • 4.2.1 Boosted Decision Trees
      • 4.2.2 Artificial Neural Networks
    • 4.3 Applications in High Energy Physics
      • 4.3.1 Signal vs Background Classification
      • 4.3.2 Particle Identification and Regression
  • 5 Search for Anomalous Higgs Pair Production with CMS
    • 5.1 Introduction
    • 5.2 Higgs Pair Production and Anomalous Couplings
    • 5.3 Analysis Strategy
    • 5.4 Trigger and Datasets
    • 5.5 Event Selection
    • 5.6 Data-Driven Background Estimation
      • 5.6.1 Hemisphere Mixing
      • 5.6.2 Background Validation
    • 5.7 Systematic Uncertainties
    • 5.8 Analysis Results
    • 5.9 Combination with Other Decay Channels
  • 6 Inference-Aware Neural Optimisation
    • 6.1 Introduction
    • 6.2 Problem Statement
    • 6.3 Method
    • 6.4 Related Work
    • 6.5 Experiments
      • 6.5.1 3D Synthetic Mixture
  • 7 Conclusions and Prospects
  • References

Statistical Learning and Inference at Particle Collider Experiments

Statistical Learning and Inference at Particle Collider Experiments

Pablo de Castro Manzano

Defended on the 29th March of 2019