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0001 \page ExampleB1Con Example B1Con
0002
0003
0004 Example of Convergence Tester
0005
0006 Koi, Tatsumi \n
0007 SLAC National Accelerator Laboratory / PPA \n
0008 tkoi@slac.stanford.eedu \n
0009
0010 This example shows how to use convergece tester in Geant4.
0011 The aim of Convergence Tester
0012 - After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
0013 The answer is usually given in a form of average value.
0014 But sometimes the value is strongly affected by single or a few events in the full calculation.
0015 In such case, we must concern about quality of the value.
0016
0017 What we must remember is
0018 - Large number of history does not valid result of simulation.
0019 - Small Relative Error does not valid result of simulation
0020 Convergence tester provides statistical information
0021 to assist establishing valid confidence intervals for Monte Carlo results for users.
0022
0023 Geometry and Physics are same to exampleB1. Please see
0024 [Example B1](../../html_B1/html/ExampleB1.html).
0025 Also run1.mac and run2.mac are like in exampleB1, with the only diffrence slightly
0026 increased number of events in run1.mac.
0027 Note that in this example, the classes with the code added for
0028 the purpose of demonstration of the Convergence Tester are defined in the namespace
0029 B1Con instead of B1 and also the executable and the test macro names are changed
0030 in exampleB1Con and exampleB1Con.in.
0031
0032
0033 Known problem:
0034 Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
0035 FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
0036
0037 ```
0038 ***********************************************************************************************************************
0039 Output example
0040
0041 // Part I.A
0042 // Basic statistics values
0043
0044 G4ConvergenceTester Output Result of DOSE_TALLY
0045 EFFICIENCY = 0.601
0046 MEAN = 4.81721e-12
0047 VAR = 2.15334e-23
0048 SD = 4.64041e-12
0049 R = 0.0304622
0050 SHIFT = 2.22459e-13
0051 VOV = 0.000166754
0052 FOM = 1238.68
0053
0054 // Part I.B
0055 // If the largeset scored events happen at next to the last event,
0056 // then how much the event effects the statistics values of the calculation
0057
0058 THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
0059 Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
0060 Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
0061 Affected R = 0.0304192 and its ratio to orignal is 0.998587
0062 Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
0063 Affected FOM = 1238.68 and its ratio to orignal is 1
0064
0065 // Part I.C
0066 // Convergence tests results
0067
0068 MEAN distribution is RANDOM
0069 r follows 1/std::sqrt(N)
0070 r is monotonically decrease
0071 r is less than 0.1. r = 0.0304622
0072 VOV follows 1/std::sqrt(N)
0073 VOV is monotonically decrease
0074 FOM distribution is not RANDOM
0075 SLOPE is not large enough
0076 This result passes 6 / 8 Convergence Test.
0077
0078 // Part II
0079 // Profile of statistics values in the history
0080
0081 G4ConvergenceTester Output History of DOSE_TALLY
0082 i/16 till_ith mean var sd r vov fom shift e r2eff r2int
0083 1 62 4.94618e-12 2.04631e-23 4.52362e-12 0.115225 0.00313634 86.5745 -1.73435e-14 0.619048 0.00976801 0.00329797
0084 2 124 4.69364e-12 2.10698e-23 4.59018e-12 0.0874712 0.001597 150.228 3.11143e-13 0.6 0.00533333 0.00225666
0085 3 187 4.72161e-12 2.14009e-23 4.62612e-12 0.0714575 0.00101852 225.105 3.1009e-13 0.590426 0.00368986 0.00138916
0086 4 249 4.95617e-12 2.13982e-23 4.62582e-12 0.0590299 0.000690138 329.865 9.71971e-14 0.62 0.00245161 0.00101898
0087 5 312 4.8529e-12 2.13482e-23 4.62041e-12 0.0538155 0.000573301 396.887 1.95662e-13 0.607029 0.00206827 0.000818582
0088 6 374 5.14255e-12 2.15736e-23 4.64474e-12 0.046641 0.000432121 528.379 -6.42963e-14 0.637333 0.00151743 0.000652145
0089 7 437 5.03849e-12 2.13484e-23 4.62043e-12 0.0438173 0.000379317 598.673 2.54207e-14 0.636986 0.00130112 0.000614447
0090 8 499 4.96962e-12 2.1429e-23 4.62914e-12 0.0416574 0.000329007 662.364 9.27708e-14 0.63 0.0011746 0.000557264
0091 9 562 4.91513e-12 2.14709e-23 4.63367e-12 0.0397316 0.000285324 728.13 1.33544e-13 0.623446 0.0010728 0.000502991
0092 10 624 4.82995e-12 2.13825e-23 4.62412e-12 0.0382954 0.000272664 783.766 2.19101e-13 0.616 0.000997403 0.000466792
0093 11 687 4.79197e-12 2.13975e-23 4.62574e-12 0.0368022 0.000251788 848.661 2.48547e-13 0.606105 0.000944593 0.000407838
0094 12 749 4.77183e-12 2.15116e-23 4.63807e-12 0.0354912 0.000227501 912.513 2.6728e-13 0.601333 0.000883962 0.000373986
0095 13 812 4.76087e-12 2.14479e-23 4.63119e-12 0.0341162 0.000212259 987.548 2.70437e-13 0.597786 0.000827601 0.000334885
0096 14 874 4.81359e-12 2.13296e-23 4.6184e-12 0.0324353 0.0001976 1092.56 2.14521e-13 0.603429 0.000751082 0.000299767
0097 15 937 4.82018e-12 2.14558e-23 4.63204e-12 0.0313767 0.000181379 1167.52 2.18545e-13 0.601279 0.000706952 0.000276498
0098 16 999 4.81721e-12 2.15334e-23 4.64041e-12 0.0304622 0.000166754 1238.68 2.22459e-13 0.601 0.000663894 0.000263125
0099
0100
0101 **************************************************************************************************************************
0102 ```
0103
0104 Reference of this Convergence tests: \n
0105 MCNP(TM) -A General Monte Carlo N-Particle Transport Code \n
0106 Version 4B \n
0107 Judith F. Briesmeister, Editor \n
0108 LA-12625-M, Issued: March 1997, UC 705 and UC 700 \n
0109 CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS \n
0110 VI. ESTIMATION OF THE MONTE CARLO PRECISION \n
0111
