Gabriele Cosmo
110 lines
3.8 KiB
Markdown
110 lines
3.8 KiB
Markdown
\page Examples_biasing Category "biasing"
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## B01, B02 and B03
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B01, B02 and B03 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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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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Scoring is carried out using the built-in Multifunctional detectors. For
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parallel geometries this requires a special scoring physics process.
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See examples/extended/runAndEvent (especailly RE05) for clarification.
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### Known problems - should not happen
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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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Short description of importance sampling and scoring:
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https://geant4.web.cern.ch/collaboration/working_groups/geometryTransport/#development-documents (Under the Event Biasing & Tallies Section)
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### \ref ExampleB01
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The example uses importance sampling or the weight window technique
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according to an input parameter.
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### \ref ExampleB02
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This example uses a parallel geometry to define G4GeometryCell objects
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for scoring and importance sampling. The output should be equivalent to B01.
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A modular approach is applied to the physicslist and the extension for biasing.
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The parallel geometry is included in this extension.
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### \ref ExampleB03
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This example uses a parallel geometry to define G4GeometryCell objects
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for scoring and importance sampling. The output should be statistically
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equivalent to B02 (and B01).
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This demonstrates a customised "flat" physics implementation with the addition
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of biasing. Complementary approach to the modular physics lists of B01 and B02
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## Generic biasing examples GB01 - GB06
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These examples illustrate the usage of a biasing scheme implemented since
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version Geant4 10.0.
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The scheme is meant to be extensible, not limited to these six examples.
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### \ref ExampleGB01
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This example illustrates how to bias process cross-sections in this scheme.
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### \ref ExampleGB02
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Illustrates a force collision scheme similar to the MCNP one.
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### \ref ExampleGB03
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Illustrates geometry based biasing.
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### \ref ExampleGB04
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Illustrates a bremsstrahlung splitting.
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### \ref ExampleGB05
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Illustrates a "splitting by cross-section" technique: a splitting-based
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technique using absorption cross-section to control the neutron population.
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### \ref ExampleGB06
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Illustrates the usage of parallel geometries with generic biasing.
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### \ref ExampleGB07
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Illustrates the usage of leading particle biasing with generic biasing.
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## Reverse MonteCarlo Technique example
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### \ref ExampleReverseMC01
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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 \ref ExampleReverseMC01 Example README page
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\endlink.
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