• Lead the maintenance and development of the FCC analysis software ecosystem
  • Explore, validate, and integrate new technologies — ML, statistical tooling, kinematic fitting, vertex fitting, …
• Develop mechanisms to produce datasets that are framework-independent and easy to analyse
  • Define dataset content in coordination with Physics, Detector Concepts, and MC Production, …
• Centrally maintained flagship analyses ready to run with any detector configuration
• Ensure adequate visualisation tools covering all use cases: geometry and simulation debugging, reconstruction development, outreach
• Provide user support and comprehensive documentation

• Main analysis framework for the FCC collaboration
  • Built on ROOT RDataFrame — declarative, multi-threaded
  • Reads EDM4hep data natively
  • C++ kernels with a Python user interface
• Served the Feasibility Study Report phase well
  • Fast simulation studies, parametric performance analyses
  • Now evolving to meet the demands of the Reference Design phase — full simulation, larger datasets, stricter reproducibility
• Manages the full analysis chain
  • Dataset metadata resolution
  • Local & distributed execution
  • Staged and histmaker analysis styles
• New: Job class — one unit of local work → one output ROOT file (#514)
  • Encapsulates RDataFrame setup, event counts, and benchmarking
• Distributed in the Key4hep stack:
  source /cvmfs/sw.hsf.org/key4hep/setup.sh
• github.com/HEP-FCC/FCCAnalyses

• Analysis is composed of functions/functors operating on RDataFrame columns — ideally small and stateless
• Two layers of analyzers:
  • External (ROOT RVec, EDM4hep utils)
  • FCCAnalyses standard library — encourage upstream contributions
• Analysis-specific extensions via .hxx header
  • JIT-compiled by ROOT at startup — no extra build step
  • Old CMake-based extension method deprecated (#477, #500)
• Fork development model creates analyzer copies across groups
  • Goal: reduce duplication by upstreaming shared functions
• Truth studies enabling set of analyzers new
• podio::DataSource — native EDM4hep reading in RDataFrame
  • Lazy reading being added to ROOTReader and RNTupleReader — podio#949 new

Staged

• Analysis split into multiple sequential stages — each writing intermediate output to disk
• Often requires running algorithms typically done at reconstruction level — e.g. vertexing, jet clustering
• Specialised ML training stage often inserted between stages — not managed by FCCAnalyses; inference then run back through FCCAnalyses
• Intermediate files written to disk — each stage can be re-run independently
• Well-suited for analyses working with large datasets where re-running the full chain is expensive

Histmaker

• Single-pass: histograms filled directly from EDM4hep input
• No intermediate ntuples — lower disk footprint
• Uses RDataFrame RunGraphs for concurrent processing of all samples
• Good for exploratory work and quick iteration on observables
• Well-suited for well-defined analyses with a fixed set of observables

Integrated within FCCAnalyses

Distributed execution driven from within FCCAnalyses

• HTCondor (CERN) — production-ready
  • fccanalysis submit ana.py — example
  • Widely used in combination with centrally produced samples
• DIRAC / iLCDirac — grid submission
  • iLCDirac application for FCCAnalyses planned
• RDataFrame distributed module
  • Apache Spark, Dask as possible backends — not yet used at FCC
• Future: Slurm and other batch platforms

External workflow management

FCCAnalyses used as an executable orchestrated from outside

• Flare — b2luigi-based orchestration
  • Handles complex multi-stage analysis pipelines
  • Integrates directly with Key4hep executables
  • Runs on HTCondor, Slurm and LSF — DIRAC (via gbasf2) and others possible
• Other workflow tools possible — Key4hep has no preferred tool

• Analysis registries — FCCeePhysicsStudies and FCChhPhysicsPerformance
  • Community hubs for analysis code, results and documentation
• eedE — EDM4hep Event Data Explorer
  • Web-based, interactive EDM4hep event content exploration
• CMS Combine — likelihood-based fits, limit setting, and hypothesis testing on FCC outputs
  • GSoC 2026: proper integration into the Key4hep stack and FCCAnalyses in progress
• FCC Physics Events API — programmatic access to dataset metadata in progress
• Dataset Request System — community-driven dataset requests in progress
• Improve plotting tools — effort started, may involve external tools
• Upstream improvements: TupleWriter in k4FWCore, podio::DataSource performance
• Full simulation flagship analyses will drive requirements in the Reference Design phase
• Interoperability with the broader HEP analysis ecosystem is a key goal
  • Overcoming silos: Key4hep ↔ PyHEP ↔ DiracOS ↔ Rucio

FCCAnalyses

• Flagship analyses for detector benchmarking
  • Centrally maintained analyses covering key physics processes, runnable on any detector configuration to benchmark performance
  • Exact scope, ownership, and hosting still to be defined — need to understand how to integrate with FCCeePhysicsStudies and FCChhPhysicsPerformance
• Make FCCAnalyses more robust
  • Overhaul analyzers: EDM4hep objects (normal and decayed), ROOT vectors, std::vectors
  • Merge staged and histmaker styles
  • Revive plotting capabilities
  • Support web-based environments (SWAN)
• ML as first-class citizen
  • Streamline current implementation; adopt TMVA SOFIE
  • Join ML developments in Key4hep
• Event weights & systematic uncertainties support
• Redesign plotting — quick plots, ratio plots, publication-level

Ecosystem

• Expand distributed computing
  • GRID (DIRAC/iLCDirac), Slurm, GPU resources
  • From within FCCAnalyses or via Flare / other workflow tools
  • FCCAnalyses on non-CERN computing resources
• Overcome software silos
  • Streamline NTuple production
  • Improve podio::DataSource performance
  • Analysis-level data model — observables, file format
• Robust analysis APIs & full dataset provenance
• Fitting tools integration — CMS Combine, zfit, RooFit, …
• Support ALEPH/LEP data workflows — legacy data as reference for FCCee physics
• Keep up with ROOT: ROOT's "R" classes
  • RNTuple support — centrally produced files expected from the fall2026 production campaign

• Phoenix @ FCC
  • Developed under HSF — several FCC detectors participating
  • Web-based 3D event display; VR/AR capabilities
  • Primarily targeted towards outreach
  • Improved EDM4hep loader new
• eedE — EDM4hep Event Data Explorer
  • Web-based; Pixi.js rendering engine
  • Interactive exploration of EDM4hep event content
  • Interface overhauled new — zooming, object selector, visual filtering, …
• Pandora Monitoring
  • Traditional ROOT-based event display
  • Heavily tied to Pandora PFA
• k4CEDViewer
  • Port of CED Viewer from iLCSoft to Key4hep
  • Traditional OpenGL-based application

• JSROOT
  • Part of the ROOT project — web-based
  • Primarily visualizes histograms and TTrees
  • Can also display detector geometries
• Geant4 Qt visualization
  • Well-established tool for Geant4 geometry inspection
  • Traditional Qt-based application
• Julia — visualization using Julia in progress
  • Exploratory work on event display and visualisation via Julia — status report
• Other specialized tools
  • calodisplay — ALLEGRO calorimeter event display
  • FCCAnalyses — RDataFrame analysis graph generation

• No single tool for debugging and development of simulation, reconstruction, and analysis
  • Support and contribute to development of existing tools
  • Avoid implementing specialized tools targeting specific sub-detectors or use cases
• Geometry conversion
  • ROOT to glTF conversion currently rudimentary
  • pyg4ometry seems promising
  • Join efforts in the Sim/Reco groups
• Reach out to other (N)HEP experiments
  • HSF Visualization WG no longer active
  • Phoenix adopted by more experiments
• OS graphics stack interoperability
  • Wayland replacing Xorg on Linux
  • Xorg/OpenGL forwarding on macOS becoming problematic

• Particles
  • MCParticle — generator-level particle access
  • ReconstructedParticle — reco particle kinematics & selection
  • ReconstructedParticle2MC, ReconstructedParticle2Track — associations
• Tracks
  • ReconstructedTrack, TrackUtils — track parameters & utilities
• Jets
  • JetClusteringUtils — FastJet interface
  • JetConstituentsUtils, JetFlavourUtils, JetTaggingUtils

• Vertexing
  • VertexFinderActs, VertexFinderLCFIPlus
  • VertexFitterActs, VertexFitterSimple
• Smearing & fast simulation
  • SmearObjects, Smearing
• ML inference
  • WeaverUtils — ONNX-based neural network inference
• Calorimetry & utilities
  • CaloNtupleizer, EventFilter, Algorithms
• FCC-hh specific: Analysis_FCChh