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FLUKA and the Virtual Monte Carlo

FLUKA and the Virtual Monte Carlo. Andreas Morsch For the ALICE Offline Group CERN, Geneva, Switzerland. Computing in High Energy and Nuclear Physics 13-17 February 2006, T.I.F.R., Mumbai, India. What is FLUKA ?. FLUKA Particle Transport Monte Carlo Code

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FLUKA and the Virtual Monte Carlo

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  1. FLUKA and the Virtual Monte Carlo Andreas Morsch For the ALICE Offline Group CERN, Geneva, Switzerland Computing in High Energy and Nuclear Physics 13-17 February 2006, T.I.F.R., Mumbai, India

  2. What is FLUKA ? • FLUKA Particle Transport Monte Carlo Code • Has evolved since long into a mature system. • Evolution based on thorough physics validation. • Almost unique capabilities for simulating hadronic interactions including low-energy neutron transport • Its state of the art physics capabilities comprise1 • Hadron-hadron, hadron-nucleus, and g-nucleus interactions 0-104 TeV • Nucleus-nucleus interactions 0-104 TeV/n • Electromagnetic and µ interactions 1 keV-104 TeV • Neutrino interactions and nucleon decays • FLUKA has proven capabilities in1: • Accelerator design and shielding (standard tool at CERN for beam-machine and Radioprotection studies • Dosimetry and hadro-therapy • Space radiation and cosmic ray showers in the atmosphere (Support by NASA, “de facto” standard tool for all aircraft dosimetry studies in Europe) 1L. S. Pinsky, “Update on the Status of the FLUKA Monte Carlo Transport Code”, CHEP06 [420].

  3. How is FLUKA used traditionally ? • For radiation studies • Combinatorial geometry based on Boolean operation • In Alice: coarse geometry O(1000) volumes • Task simplified using the ALIFE2 script developed by ALICE Offline • Definition of scoring volumes (binning) for dose, fluence, star-densities, … • Output in form of text files • For full detector simulations • FLUKA is not a tool-kit • No homogenous interface for user hit generation and stepping actions. • Access to particle information during stepping • Through user routines called during transport • Direct access to common block variables 2A. Morsch, “ALIFE: A Geometry Editor and Parser for FLUKA”, ALICE-INT-1998-29

  4. Integration of FLUKA into detector simulation frame-work • Advantages • Full detector simulation and radiation studies using the same detailed geometry • Re-use of code for detector response simulation as already developed for Geant3 • Integration has been achieved using the • Virtual Monte Interface3 and • The Root geometry modeler TGeo4 3http://root.cern.ch/root/vmc/VirtualMC.html 4http://root.cern.ch

  5. Virtual MC Concept • Transport MC transparent to the user application • Base class TVirtualMC User Code VMC TGeo Input GEANT3 VMC GEANT3 Particles Hits GEANT4 GEANT4 VMC FLUKA FLUKA VMC Output

  6. TVirtualMCApplication TVirtualMC TVirtualMCStack TVirtualMCDecayer UserStack Pythia6 TGeant3 UserApplication TGeant4 TFluka VMC Class Design • But also the user application has to be transparent to the transport MC • Base classes TVirtualMCApplication, TVirtualMCStack, TVirtualMCDecayer

  7. FLUKA VMC implementation • Implementation of TVirtualMC methods • Define the TVirtualMC ↔TVirtualMCApplication calling sequence • Cannot be enforced by the VMC Interface • Avoid hidden dependencies on the application • Has to be taken into account by any new gfof TVirtualMCApplication • Needs detailed documentation (UML).

  8. Geometry Configuration Stepping FLUKA Magnetic Field Particle Source Stacking VMC methods cover the following categories • In a traditional “FORTRAN” frame work • User routines • Common Blocks • Configuration Files

  9. TFluka TFluka TFluka TFluka TFlukaMCGeometry TGeoMCGeometry TFlukaScoringOptions TFlukaConfigOptions Class Structure TVirtualMC Geometry TFluka Physics Configuration TVirtualMC realisation Helper Classes for delegation of tasks

  10. TFluka TFluka TFlukaMCGeometry TGeoMCGeometry Interface to FLUKA:Geometry and Navigation idnrwr g1wr g1rtwr conhwr inihwr jomiwr lkdbwr lkfxwr lkmgwr lkwr nrmlwr rgrpwr isvhwr magfld TVirtualMC TFluka User Application TGeo (1) Geometry Definition FLUKA (2) Navigation Interface implemented by A. Gheata

  11. TFluka TFluka TFluka TFlukaCerenkov TFlukaScoringOption TFlukaConfigOption Interface to FLUKA: Physics Configuration TVirtualMC TFluka User Application Text Input FLUKA

  12. TFluka Interface to FLUKA:Stacking and Stepping Actions TVirtualMC eedraw endraw mgdraw sodraw usdraw source abscff dffcff queffc rflct rfrndx stupre stuprf TVirtualMCApplication TFluka TVirtualMCStack (3) (2) • Role of TVirtualMCStack • Primary particle source for transport • “Mirror” of secondaries created by FLUKA • Source of user created particles (ex. feedback photons, TR photons) Common Blocks FLUKA (1)

  13. TVirtualMC ↔TVirtualMCApplication calling sequence:Initialisation TVirtualMCApplication Detector Code User Application TVirtualMC InitMC() Init () (1) Geometry Creation AddParticles() ConstructGeometry() CreateGeometry() InitGeometry() Init() BuildPhysics()

  14. TVirtualMC ↔TVirtualMCApplication calling sequence: Simulation Run User Application TVirtualMC Generator Detector Code TVirtualMCApplication ProcessRun() BeginEvent() GeneratePrimaries() (2) Particle Generation (3) Stepping Generate() PreTrack() Field() Stepping() StepManager() PostTrack()

  15. A word of caution … • By its very nature an interface to a particle transport code cannot completely hide implementation choices. • Example: (Many-) Particle transport is parallel in nature and is sequenced by the transport code. The order of transport is an implementation choice exposed to the user. • Handling of particles that have fallen below the energy cut for transport is another example. • This can cause differences an the level of “Hits” but should disappear on the level of “Digits”.

  16. G3/FLUKA: Differences in Stepping Behavior Sensitive Volume 2 1 Geant 3 1: entering 1: exiting 2: entering 2: exiting FLUKA 1: entering 1: disappeared 2: entering 2: exiting 1: entering 1:exiting 2 hits 3 hits

  17. Validation • Validation of geometry navigation via TGeo • Standard benchmark tests provided by FLUKA authors • Technical validation of the VMC implementation • Comparison with G3 results • Physics validation • Comparison with test-beam data

  18. Z R Al-Au-Al thin layers, low energy electron transport • Trivial geometry, simulate EM-cascade • No field • 1 MeV electrons along Z, all energy lost in material • Comparison of shower profiles • Identical run conditions • Longitudinal and radial energy deposition distributions

  19. Electron transport in thin layers • 1000 electrons at 1 MeV, EM cascades • Same final random number after simulations with FLUKA native and TFluka • The same for all 3 tested examples

  20. log10(step/cm) FLUKA/G3 Comparison • Good agreement where it is expected: • Photons in electromagnetic shower FLUKA VMC G3 VMC log10(E/GeV)

  21. Comparison with test-beam data ongoing Silicon Pixel Detector

  22. Conclusions • FLUKA VMC implementation completed • Testing well advanced • TGeo/FLUKA validation completed • Good agreement with G3 and Testbeam • FLUKA VMC will be used in the next ALICE Physics data challenge

  23. Many have contributed to this project. Special thanks to: • Abrahantes Quintana, F. Carminati, B. Dalena, R. Diaz Valdes, A. Fasso, • E. Futo, A. Gheata, I. Hrivnacova, M. Gheata, I. Gonzalez Caballero, • E. Lopez Torres, M. Lopez Noriega, D. Stocco

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