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| 2 | Examples for event biasing |
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| 3 | -------------------------- |
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| 4 | |
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| 5 | This directory includes example applications to demonstrate the usage of |
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| 6 | different variance reduction techniques supported in Geant4, or possible |
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| 7 | from the user applications. |
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| 8 | |
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| 9 | |
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| 10 | General remark to variance reduction |
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| 11 | ------------------------------------ |
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| 12 | The tools provided for importance sampling (or geometrical splitting and |
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| 13 | Russian roulette) and for the weight window technique require the user to |
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| 14 | have a good understanding of the physics in the problem. This is because |
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| 15 | the user has to decide which particle types have to be biased, define the |
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| 16 | cells (physical volumes, replicas) and assign importances or weight |
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| 17 | windows to that cells. If this is not done properly it can not be |
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| 18 | expected that the results describe a real experiment. The examples given |
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| 19 | here only demonstrate how to use the tools technically. They don't intend |
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| 20 | to produce physical correct results. |
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| 21 | |
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| 22 | General remark to scoring |
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| 23 | ------------------------- |
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| 24 | A interface G4VScorer is provided for the user. The user may create his |
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| 25 | own class to perform the desired scoring. The user defined class |
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| 26 | therefore should inherit from the interface G4VScorer. |
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| 27 | An example of an implementation of a scorer is G4Scorer |
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| 28 | which may be found in source/event. |
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| 29 | The scoring in these examples is done with a G4Scorer. |
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| 30 | The variance reduction techniques and scoring do not support all options |
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| 31 | of the Geant4 geometry. It only supports physical volumes and simple |
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| 32 | replicas. |
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| 33 | To identify a physical volume (or replica) objects of the class |
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| 34 | G4GeometryCell are used. Scoring is done according to these |
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| 35 | cells and importance values or the weight windows may be assigned to |
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| 36 | them. |
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| 37 | When scoring is done in a parallel geometry special action has to be taken |
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| 38 | to prevent counting of "collisions" with boundaries of the mass geometry |
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| 39 | as interactions. This is differently handled when scoring is done in the |
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| 40 | mass geometry. |
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| 41 | |
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| 42 | --> G4GeometryCell of the parallel geometry must not share boundaries with |
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| 43 | the world volume! <-- |
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| 44 | |
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| 45 | Known problems |
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| 46 | -------------- |
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| 47 | In the following scenario it can happen that a particle is not |
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| 48 | biased and it's weight is therefore not changed even if it crosses |
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| 49 | a boundary where biasing should happen. |
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| 50 | Importance and weight window sampling create particles on boundaries |
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| 51 | between volumes. If the GPIL method of a physical process returns |
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| 52 | 0 as step length for a particle on a boundary and if the PostStepDoIt of |
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| 53 | that process changes the direction of the particle to go back in the |
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| 54 | former volume the biasing won't be invoked. |
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| 55 | This will produce particles with weights that do not correspondent to the |
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| 56 | importance of the current volumes. |
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| 57 | |
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| 58 | Further information: |
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| 59 | -------------------- |
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| 60 | Short description of importance sampling and scoring: |
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| 61 | http://cern.ch/geant4/working_groups/geometry/biasing/Sampling.html |
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| 62 | |
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| 63 | Example B01 |
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| 64 | =========== |
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| 65 | |
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| 66 | The example uses importance sampling or the weight window technique |
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| 67 | according to an input parameter. It uses scoring in both cases. |
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| 68 | Importance values or weight windows are defined according to the mass |
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| 69 | geometry. In this example the weight window technique is configured such |
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| 70 | that it behaves equivalent to importance sampling: The window is actually |
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| 71 | not a window but simply the inverse of the importance value and only |
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| 72 | one energy region is used that covers all energies in the problem. |
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| 73 | The user may change the weight window configuration by changing the |
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| 74 | initialization of the weight window algorithm in example,cc. |
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| 75 | Different energy bounds for the weight window technique may be specified |
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| 76 | in B01DetectorConstruction. |
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| 77 | |
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| 78 | The executable takes one optional argument: 0 or 1. Without argument or |
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| 79 | with argument: 0, the importance sampling is applied with argument: 1, |
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| 80 | the weight window technique is applied. |
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| 81 | |
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| 82 | |
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| 83 | Example B02 |
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| 84 | =========== |
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| 85 | |
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| 86 | This example uses a parallel geometry to define G4GeometryCell objects |
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| 87 | for scoring and importance sampling. In addition it customizes |
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| 88 | the scoring. In this example one scorer creates a histogram. |
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| 89 | |
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| 90 | Compiling and running |
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| 91 | --------------------- |
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| 92 | Can be compiled and executed on a RedHat-7.3 system with gcc-3.2.3 |
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| 93 | compiler and the tcsh shell. |
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| 94 | To compile this example you need AIDA 3.2.1 installed. To link |
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| 95 | and run it you need a AIDA compliant analysis package. The |
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| 96 | GNUmakefile of this example shows how to use AIDA through PI as |
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| 97 | analysis interface. |
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| 98 | Histograms are saved in HBOOK format. It can be displayed with PAW or |
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| 99 | compatible packages. |
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| 100 | You need to set the following variables in your environment: |
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| 101 | "G4ANALYSIS_USE" |
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| 102 | "PI_BASE_DIR" (where PI has been installed) |
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| 103 | Finally, source the script setupPI.csh. |
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| 104 | |
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| 105 | Now you should be able to run gmake and to run exampleB02. |
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| 106 | |
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| 107 | The example stores the plot in the file b02.hbook. |
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| 108 | To look at the histogram using lizard you also may use PI 1.2.1 |
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| 109 | http://cern.ch/PI. |
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| 110 | |
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| 111 | |
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| 112 | Example B03 |
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| 113 | =========== |
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| 114 | |
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| 115 | This example uses Geant4 and in particular importance sampling and |
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| 116 | scoring through python. It creates a simple histogram. It's meant |
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| 117 | to demonstrate how to use a customized scorer and importance sampling |
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| 118 | in combination with a scripting language, python. |
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| 119 | Geant4 code is executed from a python session. Therefore, swig is used |
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| 120 | to create python shadow classes and to generate the code necessary to |
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| 121 | use the Geant4 libraries from a python session. |
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| 122 | It can be built and run using PI: http://cern.ch/PI. |
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| 123 | At the end a histogram called "trackentering.hbook" is created and can be |
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| 124 | displayed using standard packages (such as PAW). |
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| 125 | |
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| 126 | Building, compiling and running |
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| 127 | ------------------------------- |
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| 128 | |
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| 129 | You need to set the following variables in your environment: |
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| 130 | "G4ANALYSIS_USE" |
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| 131 | "PI_BASE_DIR" (where PI has been installed) |
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| 132 | "SWIG_BASE_DIR" (where SWIG 1.3.15 has been installed) |
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| 133 | Finally source the script setupPI.csh. |
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| 134 | |
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| 135 | You may run gmake now. |
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| 136 | You should be able to execute the file B03RunApplication.py from your |
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| 137 | shell or from a lizard session now. |
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| 138 | |
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| 139 | At the moment the plotting is not available using a python script, but |
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| 140 | it is planned in future releases. A histogram is created and later displayed |
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| 141 | using standard analysis packages. |
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| 142 | |
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| 143 | To clean all the added files, just type gmake clean_all. |
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| 144 | |
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| 145 | Files in B03; |
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| 146 | B03Application.py: Is a example class utilizing importance sampling |
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| 147 | and scoring using python. |
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| 148 | B03RunApplication.py: Is a python script running the example. |
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| 149 | It may be executed from the shell or in a python session. |
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| 150 | B03App.py: Is created by swig using swig. |
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