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0001
0002 Example of Convergence Tester
0003
0004 Koi, Tatsumi
0005 SLAC National Accelerator Laboratory / PPA
0006 tkoi@slac.stanford.eedu
0007
0008 This example shows how to use convergece tester in Geant4.
0009 The aim of Convergence Tester
0010 After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
0011 The answer is usually given in a form of average value.
0012 But sometimes the value is strongly affected by single or a few events in the full calculation.
0013 In such case, we must concern about quality of the value.
0014 What we must remember is
0015 Large number of history does not valid result of simulation.
0016 Small Relative Error does not valid result of simulation
0017 Convergence tester provides statistical information
0018 to assist establishing valid confidence intervals for Monte Carlo results for users.
0019
0020 Geometry and Physics are same to exampleB1. Please see README.B1
0021 Also run1.mac and run2.mac are like in exampleB1, with the only diffrence slightly
0022 increased number of events in run1.mac.
0023 Note that in this example, the classes with the code added for
0024 the purpose of demonstration of the Convergence Tester are defined in the namespace
0025 B1Con instead of B1 and also the executable and the test macro names are changed
0026 in exampleB1Con and exampleB1Con.in.
0027
0028 Known problem:
0029 Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
0030 FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
0031
0032 ***********************************************************************************************************************
0033 Output example
0034
0035 // Part I.A
0036 // Basic statistics values
0037
0038 G4ConvergenceTester Output Result of DOSE_TALLY
0039 EFFICIENCY = 0.601
0040 MEAN = 4.81721e-12
0041 VAR = 2.15334e-23
0042 SD = 4.64041e-12
0043 R = 0.0304622
0044 SHIFT = 2.22459e-13
0045 VOV = 0.000166754
0046 FOM = 1238.68
0047
0048 // Part I.B
0049 // If the largeset scored events happen at next to the last event,
0050 // then how much the event effects the statistics values of the calculation
0051
0052 THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
0053 Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
0054 Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
0055 Affected R = 0.0304192 and its ratio to orignal is 0.998587
0056 Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
0057 Affected FOM = 1238.68 and its ratio to orignal is 1
0058
0059 // Part I.C
0060 // Convergence tests results
0061
0062 MEAN distribution is RANDOM
0063 r follows 1/std::sqrt(N)
0064 r is monotonically decrease
0065 r is less than 0.1. r = 0.0304622
0066 VOV follows 1/std::sqrt(N)
0067 VOV is monotonically decrease
0068 FOM distribution is not RANDOM
0069 SLOPE is not large enough
0070 This result passes 6 / 8 Convergence Test.
0071
0072
0073 // Part II
0074 // Profile of statistics values in the history
0075
0076 G4ConvergenceTester Output History of DOSE_TALLY
0077 i/16 till_ith mean var sd r vov fom shift e r2eff r2int
0078 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
0079 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
0080 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
0081 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
0082 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
0083 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
0084 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
0085 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
0086 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
0087 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
0088 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
0089 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
0090 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
0091 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
0092 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
0093 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
0094
0095 **************************************************************************************************************************
0096
0097 Reference of this Convergence tests
0098 MCNP(TM) -A General Monte Carlo N-Particle Transport Code
0099 Version 4B
0100 Judith F. Briesmeister, Editor
0101 LA-12625-M, Issued: March 1997, UC 705 and UC 700
0102 CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS
0103 VI. ESTIMATION OF THE MONTE CARLO PRECISION
0104
