Other GEANT4 capabilities

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Other GEANT4 capabilities

• Event biasing
• Parameterisation (fast simulation)
• Scoring
• Persistency
• Parallelisation and integration in a
  distributed computing environment

Alex Howard

Susanna Guatelli
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Ackowledgements

• Most of this material has been provided by
  Makoto Asai, Tsukasa Aso and Jane Tinslay
• SLAC 2006 course

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Fast simulation
Geant4 allows to perform full and fast simulation
in the same environment

• The parameterisation process produces a direct
  detector response, from the knowledge of particle
  and volume properties
• hits, digis, reconstructed-like objects (tracks,
  clusters etc.)
• Great flexibility
• activate fast /full simulation by detector
• example full simulation for inner detectors,
  fast simulation for calorimeters
• activate fast /full simulation by geometry region
• example fast simulation in central areas and
  full simulation near cracks
• activate fast /full simulation by particle type
• example in e.m. calorimeter, e/g
  parameterisation full simulation of hadrons
• parallel geometries in fast/full simulation
• example inner and outer tracking detectors
  distinct in full simulation, but handled together
  in fast simulation

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Event biasing

• Geant4 provides facilities for event biasing
• The effect consists in producing a small number
  of secondaries, which are artificially recognized
  as a huge number of particles by their
  statistical weights
• Event biasing can be used, for instance, for the
  transportation of slow neutrons or in the
  radioactive decay simulation
• Various variance reduction techniques available

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Event biasing techniques

• Production cuts / threshold
• This is a biasing technique most popular for
  many applications
• Geometry based biasing
• Importance weighting for volume/region
• Duplication or sudden death of tracks
• Leading particle biasing
• Taking only the most energetic (or most
  important) secondary
• Primary event biasing
• Biasing primary events and/or primary particles
  in terms of type of event, momentum distribution,
  etc.
• Forced interaction
• Force a particular interaction, e.g. within a
  volume
• Enhanced process or channel
• Increasing cross section for a process
• Physics based biasing
• Biasing secondary production in terms of particle
  type, momentum distribution, cross-section, etc.

gt Weight on track / event.
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Current features in Geant4

• Partial MARS migration
• n, p, pi, K (lt 5 GeV)
• Since Geant4 0.0
• General particle source module
• Primary particle biasing
• Since Geant4 3.0
• Radioactive decay module
• Physics process biasing in terms of decay
  products and momentum distribution
• Since Geant4 3.0
• Geometry based biasing
• Weight associating with real volume or artificial
  volume
• Since Geant4 4.2
• Weight cutoff and weight window
• Since Geant4 5.2
• Hadronic process module
• Cross-section biasing (PhotoInelactic,ElectronNucl
  ear,PositronNuclear)
• Leading particle biasing for hadronic processes
• Since Geant4 7.0

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Leading particle biasing

• Simulating a full shower is an expensive
  calculation
• Instead of generating a full shower, trace only
  the most energetic secondary
• Other secondary particles are immediately killed
  before being stacked
• Convenient way to roughly estimate, e.g. the
  thickness of a shield
• Physical quantities such as energy are not
  conserved for each event

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Geometric Biasing
The purpose of geometry based event biasing is
to save computing time by sampling less often
the particle histories entering less important
geometry regions, and more often in more
important regions.
Importance sampling technique Weight window
technique
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Variance Reduction

• Use variance reduction techniques to reduce
  computing time taken to calculate a result with a
  given variance
• Want to increase efficiency of the Monte Carlo
• Measure of efficiency is given by
• s variance on calculated quantitiy
• T computing time

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• Remove bias introduced from generating multiple
  secondaries by assigning a statistical weight to
  each secondary
• N number of secondary photons
• Preserves photon energy and angluar distributions
• Currently, no default bremsstrahlung splitting in
  Geant4 toolkit
• User can implement bremsstrahlung splitting by
• Directly modifying bremsstrahlung source code
• Using G4WrapperProcess
• May be slightly less efficient
• Less invasive
• Easier to implement

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Importance sampling technique

• Importance sampling acts on particles crossing
  boundaries between importance cells.
• The action taken depends on the importance value
  assigned to the cell.
• In general, a track is played either split or
  Russian roulette at the geometrical boundary
  depending on the importance value assigned to the
  cell.

• Survival probability (P) is defined by the ratio
  of importance value. P Ipost / Ipre
• The track weight is changed to W/P.

I1
I2
W0.5
W1

• Splitting a track ( P gt 1 )
• E.g. creating two particles with half the
  weight if it moves into volume with double
  importance value.

W0.5
P 2

• Russian-roulette (P lt 1 ) in opposite direction
• E.g. Kill particles according to the survival
  probability (1 - P).

P 0.5
X
W0.5
W1
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Geometrical importance biasing

• Define importance for each geometrical region
• Duplicate a track with half (or relative) weight
  if it goes toward more important region
• Russian-roulette in another direction
• Scoring particle flux with weights
• at the surface of volumes

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Importance biasing
Analogue simulation
10 MeV neutron in thick concrete cylinder
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Examples/extended/biasing
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Biasing example B01

• Shows the importance sampling in the mass
  (tracking) geometry
• Option to show weight window
• 10 MeV neutron shielding by cylindrical thick
  concrete material
• Geometry
• 80 cm high concrete cylinder divided into 18
  slabs
• Importance values assigned to 18 concrete slabs
  in the DetectorConstruction for simplicity.
• The G4Scorer is used for the checking result
• Top level class uses the framework provided for
  scoring.

Air
Air
1 1 2 4 8 16 32 64 .. 2n
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Flux multiplied by Kinetic energy of particle
(MeV)
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Example B02

• B02 example for showing
• importance sampling in a parallel geometry
• a customized scoring making use of the scoring
  framework.
• Mass geometry consists of a 180 cm high simple
  bulk concrete cylinder
• A parallel geometry is created to hold importance
  values for slabs of width 10cm and for scoring.
• Note The parallel world volume must overlap the
  mass world volume
• The radii of the slabs is larger than the radius
  of the concrete cylinder in the mass geometry.
• The importance value is assigned to each
  G4GeometryCell
• Pairs of G4GeometryCell and importance values are
  stored in the importance store, G4IStore.
• The scoring uses the G4CellSCorer and one
  customized scorer for the last slab.
• It can be built and run using the PI
  implementation of AIDA
• For this see http//cern.ch/PI.
• At the end a histogram called b02.hbook" is
  created.

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Example B03

• Uses Geant4 importance sampling and scoring
  through python.
• It creates a simple histogram.
• It demonstrates how to use a customized scorer
  and importance sampling in combination with a
  scripting language, python.
• Geant4 code is executed from a python session.
• Note the swig package is used to create python
  shadow classes and to generate the code necessary
  to use the Geant4 libraries from a python
  session.
• It can be built and run using the PI
  implementation of AIDA
• For this see http//cern.ch/PI.
• At the end a histogram called "trackentering.hbook
  " is created.

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SCORING

• examples/extended/runAndEvent/RE02

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Extract useful information

• Given geometry, physics and primary track
  generation, Geant4 does proper physics simulation
  silently.
• You have to add a bit of code to extract
  information useful to you.
• There are two ways
• Use user hooks (G4UserTrackingAction,
  G4UserSteppingAction, etc.)
• You have full access to almost all information
• Straight-forward, but do-it-yourself
• Use Geant4 scoring functionality
• Assign G4VSensitiveDetector to a volume
• Hit is a snapshot of the physical interaction of
  a track or an accumulation of interactions of
  tracks in the sensitive (or interested) part of
  your detector.
• Hits collection is automatically stored in
  G4Event object, and automatically accumulated if
  user-defined Run object is used.
• Use user hooks (G4UserEventAction,
  G4UserRunAction) to get event / run summary

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For example

• MyDetectorConstructionConstruct()
• G4LogicalVolume myCellLog new
  G4LogicalVolume()
• G4VPhysicalVolume myCellPhys new
  G4PVParametrised()
• G4MultiFunctionalDetector myScorer new
  G4MultiFunctionalDetector(myCellScorer)
• G4SDManagerGetSDMpointer()-gtAddNewDetector(mySco
  rer)
• myCellLog-gtSetSensitiveDetector(myScorer)
• G4VPrimitiveSensitivity totalSurfFlux new
  G4PSFlatSurfaceFlux(TotalSurfFlux)
• myScorer-gtRegister(totalSurfFlux)
• G4VPrimitiveSensitivity totalDose new
  G4PSDoseDeposit(TotalDose)
• myScorer-gtRegister(totalDose)

No need of implementing sensitive detector !
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A tip for scoring

• For scoring purposes, you need to accumulate a
  physical quantity (e.g. energy deposition of a
  step) for entire run of many events. In such a
  case, do NOT sum up individual energy deposition
  of each step directly to a variable for entire
  run.
• Compared to the total sum for entire run, each
  energy deposition of single step is too tiny.
  Rounding error problem may easily happen.
• Total energy deposition of 1 million events of 1
  GeV incident particle ends up to 1 PeV (1015 eV),
  while energy deposition of each single step is
  O(1 keV) or even smaller.
• Create your own Run class derived from G4Run, and
  implement RecordEvent(const G4Event) virtual
  method. Here you can get all output of the event
  so that you can accumulate the sum of an event to
  a variable for entire run.
• RecordEvent(const G4Event) is automatically
  invoked by G4RunManager.
• Your run class object should be instantiated in
  GenerateRun() method of your UserRunAction.

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G4VPrimitiveScorer

• G4VPrimitiveScorer is an abstract base class
  representing a class to be registered to
  G4MultiFunctionalDetector.
• Geant4 provides concrete primitive scorer classes
  such as dose scoring, surface flux counting, etc.
  
• Of course, users can develop his/her own
  primitive scorer classes.
• Primitive scorers are designed to score one kind
  of quantity and generates one hits collection per
  event.
• The name of hits collection is automatically
  assigned asltMultiFunctionalDetector
  namegt/ltPrimitive Scorer namegt.The hits
  collection is maintained by G4HCofThisEvent
  object with a unique collection ID number which
  is assigned by G4SDManager.
• Each primitive scorer object must be instantiated
  with a unique name among primitive scorers
  registered in a G4MultiFunctionalDetector object.
• A primitive scorer object must not be shared by
  more than one G4MultiFunctionalDetector object.
  Otherwise, the results are mixed together.

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List of concrete primitive scorer

• Concrete Primitive Scorers ( See Application
  Developers Guide 4.4.6 )
• Track length
• G4PSTrackLength, G4PSPassageTrackLength
• Deposited energy
• G4PSEnergyDepsit, G4PSDoseDeposit
• Current/Flux
• G4PSFlatSurfaceCurrent, G4PSSphereSurfaceCurrent,G
  4PSPassageCurrent, G4PSFlatSurfaceFlux,
  G4PSCellFlux, G4PSPassageCellFlux
• Others
• G4PSMinKinEAtGeneration, G4PSNofSecondary,
  G4PSNofStep

SurfaceCurrent Count number of injecting
particles at defined surface.
SurfaceFlux Sum up 1/cos(angle) of injecting
particlesat defined surface
CellFlux Sum of L / V of injecting particles
in the geometrical cell.
V Volume
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Summary

• Geant4 offers the possibility to improve
  computing performance via fast simulation and
  biasing
• A number of biasing techniques are available but
  are the users responsibility to use the results
  correctly
• Scoring is implemented with a degree of
  flexibility to reduce hits collection and
  persistency whilst offering convenience of
  keeping tallies of common qualities
• A number of examples are available within
  the G4INSTALL/examples/extended source tree
• See documentation for more details