Gabriele Cosmo
110 lines
4.6 KiB
Plaintext
110 lines
4.6 KiB
Plaintext
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Examples for event biasing: B01 and B02
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B01 and B02 applications demonstrate the usage of different variance
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reduction techniques supported in Geant4, or possible from the user
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applications.
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General remark to variance reduction
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------------------------------------
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The tools provided for importance sampling (or geometrical splitting and
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Russian roulette) and for the weight window technique require the user to
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have a good understanding of the physics in the problem. This is because
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the user has to decide which particle types have to be biased, define the
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cells (physical volumes, replicas) and assign importances or weight
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windows to that cells. If this is not done properly it can not be
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expected that the results describe a real experiment. The examples given
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here only demonstrate how to use the tools technically. They don't intend
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to produce physical correct results.
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General remark to scoring
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-------------------------
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A interface G4VScorer is provided for the user. The user may create his
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own class to perform the desired scoring. The user defined class
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therefore should inherit from the interface G4VScorer.
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An example of an implementation of a scorer is G4Scorer
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which may be found in source/event.
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The scoring in these examples is done with a G4Scorer.
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The variance reduction techniques and scoring do not support all options
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of the Geant4 geometry. It only supports physical volumes and simple
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replicas.
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To identify a physical volume (or replica) objects of the class
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G4GeometryCell are used. Scoring is done according to these
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cells and importance values or the weight windows may be assigned to
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them.
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When scoring is done in a parallel geometry special action has to be taken
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to prevent counting of "collisions" with boundaries of the mass geometry
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as interactions. This is differently handled when scoring is done in the
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mass geometry.
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--> G4GeometryCell of the parallel geometry must not share boundaries with
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the world volume! <--
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Known problems
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--------------
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In the following scenario it can happen that a particle is not
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biased and it's weight is therefore not changed even if it crosses
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a boundary where biasing should happen.
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Importance and weight window sampling create particles on boundaries
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between volumes. If the GPIL method of a physical process returns
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0 as step length for a particle on a boundary and if the PostStepDoIt of
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that process changes the direction of the particle to go back in the
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former volume the biasing won't be invoked.
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This will produce particles with weights that do not correspondent to the
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importance of the current volumes.
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Further information:
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--------------------
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Short description of importance sampling and scoring:
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http://cern.ch/geant4/working_groups/geometry/biasing/Sampling.html
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Example B01
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===========
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The example uses importance sampling or the weight window technique
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according to an input parameter. It uses scoring in both cases.
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Importance values or weight windows are defined according to the mass
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geometry. In this example the weight window technique is configured such
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that it behaves equivalent to importance sampling: The window is actually
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not a window but simply the inverse of the importance value and only
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one energy region is used that covers all energies in the problem.
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The user may change the weight window configuration by changing the
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initialization of the weight window algorithm in example,cc.
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Different energy bounds for the weight window technique may be specified
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in B01DetectorConstruction.
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The executable takes one optional argument: 0 or 1. Without argument or
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with argument: 0, the importance sampling is applied with argument: 1,
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the weight window technique is applied.
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Example B02
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===========
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This example uses a parallel geometry to define G4GeometryCell objects
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for scoring and importance sampling. In addition it customizes
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the scoring. In this example one scorer creates a histogram.
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Compiling and running
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---------------------
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To compile this example you need AIDA installed. To link
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and run it you need a AIDA compliant analysis package.
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Histograms are saved in HBOOK format.
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You need to set the following variable in your environment:
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"G4ANALYSIS_USE"
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The example stores the plot in the file b02.hbook.
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___________________________________________________________________________
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Reverse MonteCarlo Technique example: ReverseMC01
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-------------------------------------------------
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Example ReverseMC01
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===================
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Example illustrating the use of the Reverse Monte Carlo (RMC) mode in a Geant4
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application. See details in ReverseMC01/README.
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