7.8. Advanced Topics

• 7.8.1. Rest Of Event
  • Overview
  • Basic usage
  • ROE particle type hypothesis
  • Charged PID priors
  • Selection cut based method
  • Accessing ROE particles
  • ROE masks
  • Nested ROE
  • Load ROE as a particle
  • Create ROE using MCParticles
  • MVA based cleaning

• 7.8.2. How to Veto
  • Introduction
  • Overview of the workflow
  • Examples

• 7.8.3. Flavor Tagger
  • Flavor Tagging Principle
  • Flavor Tagger Algorithm
  • Using the FlavorTagger
  • Saving to nTuples
  • Efficiency Calculation and Validation Plots
  • Tutorials
  • Functions

• 7.8.4. Deep Flavor Tagger
  • Principle
  • Algorithm
  • How to use
  • Functions

• 7.8.5. Full event interpretation
  • Algorithm overview
    • Physics
    • Key Performance Indicators
    • Hadronic, Semileptonic and Inclusive Tagging
    • Hierarchical Approach
  • Applying the FEI
  • FEI and the conditions database
  • Sphinx documentation
  • Code structure
    • config.py
    • default_channels.py
    • core.py
  • Training the FEI
    • Basic Workflow (training)
    • distributed.py
    • Restarting a training
  • FEI Training on the Grid
    • Software and Environment Setup
    • General Workflow Concept
    • Technical details
      • settings.json
      • B_generic_train.py
      • FEIAnalysisSummaryTask and FEIAnalysisTask
        • The Problem of Too Long Runtimes
      • MergeOutputsTask
      • FEITrainingTask
      • PrepareInputsTask
      • Further Comments on fei_grid_workflow.py
    • Tips and Tricks
    • Possible Improvements
      • Potential Improvements Following gbasf2 Development
  • Troubleshooting
    • Crash in the Neurobayes Library
    • Running FEI on converted Belle MC outside of KEK
    • Speeding up the FEI
  • Resources, Publications etc.

• 7.8.6. EventShape
  • Introduction
  • What is the difference between event shape and continuum suppression?
  • How to use eventShape
  • Event shape variables

• 7.8.7. Continuum suppression
  • Example usage
  • Continuum Suppression variables
  • Deep Continuum Suppression
    • Adversarial Networks

• 7.8.8. Basf2 MVA interface

• 7.8.9. PID Prior Probabilities
  • Training
    • Optional Arguments
    • Required Arguments
  • Evaluation
    • Import
    • Initialize
    • Prior Calculation
    • Getting the Priors
    • Getting the Posteriors

• 7.8.10. ksSelector
  • V0Selector
  • LambdaVeto
  • Tutorials
  • ksSelector Functions

• 7.8.11. PID Calibration Weights
  • Preparing Data Samples for Training
  • Training the Weights
    • Required Arguments
    • Optional Arguments
  • Applying Weights to Data for Performance Analysis
  • PID calibration weights on the basf2 path
  • pidDataUtils Functions

• 7.8.12. Signal Embedding
  • Documentation of the scripts:
    • Optional Arguments
    • Optional Arguments