Examples for event biasing: B01 and B02
                    ---------------------------------------

B01 and B02 applications demonstrate the usage of different variance
reduction techniques supported in Geant4, or possible from the user
applications.

General remark to variance reduction
------------------------------------
The tools provided for importance sampling (or geometrical splitting and
Russian roulette) and for the weight window technique require the user to 
have a good understanding of the physics in the problem. This is because 
the user has to decide which particle types have to be biased, define the 
cells (physical volumes, replicas) and assign importances or weight 
windows to that cells. If this is not done properly it can not be 
expected that the results describe a real experiment. The examples given 
here only demonstrate how to use the tools technically. They don't intend 
to produce physical correct results.

General remark to scoring
-------------------------
A interface G4VScorer is provided for the user. The user may create his 
own class to perform the desired scoring. The user defined class 
therefore should inherit from the interface G4VScorer. 
An example of an implementation of a scorer is G4Scorer
which may be found in source/event. 
The scoring in these examples is done with a G4Scorer.
The variance reduction techniques and scoring do not support all options
of the Geant4 geometry. It only supports physical volumes and simple 
replicas.
To identify a physical volume (or replica) objects of the class
G4GeometryCell are used. Scoring is done according to these
cells and importance values or the weight windows  may be assigned to 
them.
When scoring is done in a parallel geometry special action has to be taken
to prevent counting of "collisions" with boundaries of the mass geometry 
as interactions. This is differently handled when scoring is done in the 
mass geometry.

--> G4GeometryCell of the parallel geometry must not share boundaries with
the world volume! <--

Known problems
--------------
In the following scenario it can happen that a particle is not
biased and it's weight is therefore not changed even if it crosses
a boundary where biasing should happen.
Importance and weight window sampling create particles on boundaries 
between volumes. If the GPIL method of a physical process returns 
0 as step length for a particle on a boundary and if the PostStepDoIt of
that process changes the direction of the particle to go back in the 
former volume the biasing won't be invoked. 
This will produce particles with weights that do not correspondent to the
importance of the current volumes.

Further information:
--------------------
Short description of importance sampling and scoring:
http://cern.ch/geant4/working_groups/geometry/biasing/Sampling.html

Example B01
===========

The example uses importance sampling or the weight window technique 
according to an input parameter. It uses scoring in both cases. 
Importance values or weight windows are defined according to the mass 
geometry. In this example the weight window technique is configured such 
that it behaves equivalent to importance sampling: The window is actually 
not a window but simply the inverse of the importance value and only
one energy region is used that covers all energies in the problem.
The user may change the weight window configuration by changing the
initialization of the weight window algorithm in example,cc. 
Different energy bounds for the weight window technique may be specified 
in B01DetectorConstruction.

The executable takes one optional argument: 0 or 1. Without argument or
with argument: 0, the importance sampling is applied with argument: 1,
the weight window technique is applied.


Example B02
===========

This example uses a parallel geometry to define G4GeometryCell objects
for scoring and importance sampling. In addition it customizes
the scoring. In this example one scorer creates a histogram.

Compiling and running
---------------------
To compile this example you need AIDA installed. To link 
and run it you need a AIDA compliant analysis package.
Histograms are saved in HBOOK format.
You need to set the following variable in your environment:
   "G4ANALYSIS_USE"
The example stores the plot in the file b02.hbook.

 ___________________________________________________________________________


             Reverse MonteCarlo Technique example: ReverseMC01
             -------------------------------------------------

Example ReverseMC01
===================

Example illustrating the use of the Reverse Monte Carlo (RMC) mode in a Geant4
application. See details in ReverseMC01/README.