2. Generators, Fast Simulation and Analysis

If you want to get started with generation and analysis of fast-simulated events, you’re at the right place.

Fast simulation is currently supported through the Delphes approach. Support for the Papas approach, initially used for FCC-ee, is discontinued.

An analysis ntuple will be produced with ROOT’s RDataFrame, a simple modular event processing framework for high energy physics.

If you have any problems or questions, you can open an issue on the GitHub repository where these lessons are developed.

Contents:

• 2.1. FCC: Getting started with event generation
  • 2.1.1. Overview
  • 2.1.2. Enabling FCCSW
  • 2.1.3. Generators
    • 2.1.3.1. Overview
    • 2.1.3.2. Pythia8
    • 2.1.3.3. Whizard
    • 2.1.3.4. KKMCee
    • 2.1.3.5. BHLUMI
    • 2.1.3.6. BabaYaga
    • 2.1.3.7. Herwig
    • 2.1.3.8. MadGraph5
  • 2.1.4. Hands-on case study: ditau events with Pythia8, Whizard and KKMCee
    • 2.1.4.1. Generating ditaus with Pythia8
    • 2.1.4.2. Generating ditaus with Whizard
      • 2.1.4.2.1. LHEf to EDM4hep conversion
    • 2.1.4.3. Generating ditaus with KKMCee
      • 2.1.4.3.1. Generating HepMC events
      • 2.1.4.3.2. HepMC to EDM4hep conversion
        • 2.1.4.3.2.1. Dissection of hepmc2edm.py
        • 2.1.4.3.2.2. Running hepmc2edm.py
    • 2.1.4.4. Looking at the produced files. Comparing distributions
      • 2.1.4.4.1. Looking the MCParticle class
      • 2.1.4.4.2. Building FCCAnalyses
      • 2.1.4.4.3. Creating histograms with FCCAnalyses
        • 2.1.4.4.3.1. Dissection of histmaker_ttmm.py
        • 2.1.4.4.3.2. Running histmaker_ttmm.py
      • 2.1.4.4.4. Comparing distributions
      • 2.1.4.4.5. Possible conclusion of the exercise
• 2.2. FCC: Getting started with simulating events in Delphes
  • 2.2.1. Generate and Simulate Events with DelphesEDM4Hep
• 2.3. FCC: Analysing simulated events
  • 2.3.1. Part I: Analyse events with histmaker
  • 2.3.2. Part II: Produce a flat tree and analyse events
• 2.4. FCC: tracking and vertexing example using specific flavour decays
  • 2.4.1. Installation of FCCAnalyses
  • 2.4.2. Building a custom sub-package in FCCAnalyses
  • 2.4.3. Reconstruction of the primary vertex and of primary tracks
    • 2.4.3.1. Exercises:
  • 2.4.4. Reconstruction of displaced vertices in an exclusive decay chain: starting example
  • 2.4.5. Exercise: analysis of \(\tau \rightarrow 3 \mu\)
• 2.5. FCCAnalyses: Common problems and solutions
  • 2.5.1. Prerequisites
    • 2.5.1.1. EDM4hep event model
    • 2.5.1.2. Structure of EDM4hep files
  • 2.5.2. Overall organisation of analysis code (C++)
  • 2.5.3. Reading objects from EDM4hep
  • 2.5.4. Association between RecoParticles and MonteCarloParticles
  • 2.5.5. Navigation through the history of the MC particles
  • 2.5.6. Writing a new analyzer
    • 2.5.6.1. C++ string
    • 2.5.6.2. Using ROOT gInterpreter
    • 2.5.6.3. Inside an existing FCCAnalyses module
  • 2.5.7. Writing a new struct
  • 2.5.8. Writing a new module
  • 2.5.9. Writing your own analysis using the case-studies generator
    • 2.5.9.1. Analysis package generation
    • 2.5.9.2. Analysis package exposure to RDF
• 2.6. Central production of events
  • 2.6.1. Clone and initialisation
  • 2.6.2. Generate LHE files from gripacks
  • 2.6.3. Generate LHE files directly from MG5
  • 2.6.4. Generate STDHEP files directly from Whizard + Pythia6
  • 2.6.5. Generate EDM4hep files from the LHE and decay with Pythia8
  • 2.6.6. Generate EDM4hep files from STDHEP
  • 2.6.7. Generate EDM4hep files from Pythia8
  • 2.6.8. Expert mode
    • 2.6.8.1. Updating the database
    • 2.6.8.2. Cleaning bad jobs
    • 2.6.8.3. Update the webpage
    • 2.6.8.4. Create the sample list for analyses
    • 2.6.8.5. All in One
  • 2.6.9. Produce samples with EventProducer outside of official campaign