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Data Model: LCIO to LCIO2.0

Data Model: LCIO to LCIO2.0. Norman Graf (SLAC) ILC-CLIC Software, CERN May 28, 2009. The LOI Physics Benchmarks Process.

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Data Model: LCIO to LCIO2.0

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  1. Data Model:LCIO to LCIO2.0 Norman Graf (SLAC) ILC-CLIC Software, CERN May 28, 2009

  2. The LOI Physics Benchmarks Process • The full-detector simulation physics benchmarking requirements presented the ILC detector concepts with a large-scale, end-to-end exercise which stressed most aspects of the software systems. • Event Generation • Detector Simulation • Event Reconstruction • Physics Analysis

  3. LCIO • A common event data model and a common persistence format played a large part in the success of the LOI process for SiD and ILD. • Events from different simulation packages could be and were analyzed with software from different reconstruction and analysis frameworks (and languages.) • No need for a single monolithic framework. • Functionality of the tools themselves and common event data model more important than the framework into which they plug. • Discussions initiated on LCIO2.0 in response to user experiences to-date.

  4. LCIO Common Data Model Common IO Format Interoperable Reconstruction Geometry  missing LCIO ALCPG SiD ACFA-ILC GLD ECFA-ILC LDC slic org.lcsim MOKKA MarlinReco JUPITER Satellites Also successfully used by several experimental groups for their testbeam data.

  5. LCIO Philosophy “Simplify, simplify, simplify” Thoreau “Make everything as simple as possible, but not simpler.” Einstein Identify the key elements for an event data model appropriate to a colliding detector experiment.

  6. Monte Carlo Raw Data Digitization Cluster RawCalorimeter Hit SimCalorimeterHit LCRelation CalorimeterHit Reconstructed Particle LCRelation MCParticle TrackerRawData Track SimTrackerHit LCRelation TrackerHit Vertex TrackerData TrackerPulse LCIO Event Data Model Reconstruction & Analysis

  7. LCIO Extensions • In addition to the predefined classes, LCIO also allows users to define extensions to classes by: • defining collections of primitives • defining associations via LCRelations • define new objects via LCGenericObject

  8. LCIO2.0 • End-to-end LOI exercise and user feedback has allowed us to identify a few key places where the model could usefully be improved: • Allow additional fits for track • Add support for 2-D and 1-D hits (e.g. pixel & strip) • Introduce classes for space points and directions • additional type-safety on arguments • Additional lessons learned from the LOI, and from the merger of the LDC & GLD event models await a post-mortem later this summer.

  9. Summary • Having a common event data model has enabled an unprecedented level of cooperation and collaboration: • across concept (SiD, LDC, GLD, ILD) • across language (Fortran, Java, C++, python) • across platform (Linux, Mac, Windows) • Use of well-defined interfaces for data exchange has been more important than imposing a single framework, language or platform. C++(Geant4) sim  Java recon  C++ recon  root analysis • LCIO has been very successful so far, and we plan to continue development with LCIO2.0 • We encourage LHC tools to support LCIO (and other common formats such as GDML, AIDA, Heprep,etc.). • Support for a lightweight, interoperable geometry system would be appreciated.

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