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1
2 Examples for event biasing: B01, B02 and B03
3 --------------------------------------------
4
5 B01, B02 and B03 applications demonstrate the usage of different variance
6 reduction techniques supported in Geant4, or possible from the user
7 applications.
8
9 General remark to variance reduction
10 ------------------------------------
11 The tools provided for importance sampling (or geometrical splitting and
12 Russian roulette) and for the weight window technique require the user to
13 have a good understanding of the physics in the problem. This is because
14 the user has to decide which particle types have to be biased, define the
15 cells (physical volumes, replicas) and assign importances or weight
16 windows to that cells. If this is not done properly it can not be
17 expected that the results describe a real experiment. The examples given
18 here only demonstrate how to use the tools technically. They don't intend
19 to produce physical correct results.
20
21 General remark to scoring
22 -------------------------
23 Scoring is carried out using the built-in Multifunctional detectors. For
24 parallel geometries this requires a special scoring physics process.
25 See examples/extended/runAndEvent (especailly RE05) for clarification.
26
27 Known problems - should not happen
28 ----------------------------------
29 In the following scenario it can happen that a particle is not
30 biased and it's weight is therefore not changed even if it crosses
31 a boundary where biasing should happen.
32 Importance and weight window sampling create particles on boundaries
33 between volumes. If the GPIL method of a physical process returns
34 0 as step length for a particle on a boundary and if the PostStepDoIt of
35 that process changes the direction of the particle to go back in the
36 former volume the biasing won't be invoked.
37 This will produce particles with weights that do not correspondent to the
38 importance of the current volumes.
39
40 Further information:
41 --------------------
42 Short description of importance sampling and scoring:
43 https://geant4.web.cern.ch/collaboration/working_groups/geometryTransport/#development-documents (Under the Event Biasing & Tallies Section)
44
45 Example B01
46 ===========
47
48 The example uses importance sampling or the weight window technique
49 according to an input parameter. It uses scoring in both cases.
50 Importance values or weight windows are defined according to the mass
51 geometry. In this example the weight window technique is configured such
52 that it behaves equivalent to importance sampling: The window is actually
53 not a window but simply the inverse of the importance value and only
54 one energy region is used that covers all energies in the problem.
55 The user may change the weight window configuration by changing the
56 initialization of the weight window algorithm in example,cc.
57 Different energy bounds for the weight window technique may be specified
58 in B01DetectorConstruction.
59
60 The executable takes one optional argument: 0 or 1. Without argument or
61 with argument: 0, the importance sampling is applied with argument: 1,
62 the weight window technique is applied.
63
64 A modular approach is applied to the physicslist and the extension for biasing.
65
66 Example B02
67 ===========
68
69 This example uses a parallel geometry to define G4GeometryCell objects
70 for scoring and importance sampling. The output should be equivalent to B01.
71
72 A modular approach is applied to the physicslist and the extension for biasing.
73 The parallel geometry is included in this extension.
74
75 Example B03
76 ===========
77
78 This example uses a parallel geometry to define G4GeometryCell objects
79 for scoring and importance sampling. The output should be statistically
80 equivalent to B02 (and B01).
81
82 This demonstrates a customised "flat" physics implementation with the addition
83 of biasing. Complementary approach to the modular physics lists of B01 and B02
84
85
86 ___________________________________________________________________________
87
88
89 Generic biasing examples GB01 - GB06
90 ------------------------------------
91
92 These examples illustrate the usage of a biasing scheme implemented since
93 version Geant4 10.0.
94 The scheme is meant to be extensible, not limited to these six examples.
95
96 Example GB01:
97 =============
98
99 This example illustrates how to bias process cross-sections in this scheme.
100
101
102 Example GB02:
103 =============
104
105 Illustrates a force collision scheme similar to the MCNP one.
106
107
108 Example GB03:
109 =============
110
111 Illustrates geometry based biasing.
112
113
114 Example GB04:
115 =============
116
117 Illustrates a bremsstrahlung splitting.
118
119
120 Example GB05:
121 =============
122
123 Illustrates a "splitting by cross-section" technique: a splitting-based
124 technique using absorption cross-section to control the neutron population.
125
126
127 Example GB06:
128 =============
129
130 Illustrates the usage of parallel geometries with generic biasing.
131
132 Example GB07:
133 =============
134
135 Illustrates the usage of leading particle biasing with generic biasing.
136
137
138 ___________________________________________________________________________
139
140
141 Reverse MonteCarlo Technique example: ReverseMC01
142 -------------------------------------------------
143
144 Example ReverseMC01
145 ===================
146
147 Example illustrating the use of the Reverse Monte Carlo (RMC) mode in a Geant4
148 application. See details in ReverseMC01/README.
149
