FCCAnalyses Overview

Analysis framework build on top of ROOT RDataFrame
with input from EDM4hep

• Dependent on Key4hep Stack
• Manages input samples
• Has standard library of functions/closures
• Runs the dataframe
• Helps with histograms/plots
• Registry for the analyses

FCCAnalyses script

• Typical analysis divided into several stages
• Results between stages stored in ROOT files
• Running of the script with: fccanalysis run ana_script.py

Key4hep

FCCAnalyses is tied to the Key4hep stack

• Distributed as a Spack package
• Key4hep environment needed for running
• EDM4hep objects read directly from ROOT files
• Building from source expects Key4hep
• People pin their analysis to the particular stack version

Input samples

FCCAnalyses manages input ROOT files for the user

• Analysis operates on named samples (by process name)
• Pre-generated samples identified with production tag
• Registry of available samples available at FCC Physics Events website
• Local samples require input directory path
• Process dictionary allows further parameters: fraction, chunks, ...

Analyzers

Collection of standard functions/closures

• Users define their dataframe in a class method
• Output variables registered in a list
• Additional analyzers JIT compiled

Running of the RDF

Execution of the dataframe hidden from the user

• User can affect how the dataframe runs with global attributes
• Analysis can run locally or on HTCondor

Histograms/Plots

Last two stages of the analysis

• User specifies output histograms
• Histograms are combined into plots

Analysis registry

Central registry for the FCC-ee analyses

• In the repository FCCeePhysicsPerformance FCCee analyses are listed
• Experimental: One can create analysis package for analysis specific code

Some observations about RDF

• RDataFrame feels restricted by the FCCAnalyses
• Vast majority of users expects Pythonic interface
• Some people miss access to the event loop
• Bulk processing not very user-friendly for function/closure development
• Definition of multiple variables not possible
• Powerful abstraction which well describes the analysis

Phoenix

• Static web application
  • Independent of OS graphics stack
  • Written in TypeScript/Angular
• Developed under HSF
• Detector Geometry provided centrally
  • DD4hep Compact → ROOT → glTF
• Event data provided by user
  • EDM4hep → JSON

dmX

• Visualization of event data structure
• Uses Canvas API (2D graphics)
• Collection elements represented as info boxes

Pythia 8 | ee → ZH @ 240 GeV