| Geant4 Cross Reference |
1 1
2 Example of Convergence Tester 2 Example of Convergence Tester
3 3
4 Koi, Tatsumi 4 Koi, Tatsumi
5 SLAC National Accelerator Laboratory 5 SLAC National Accelerator Laboratory / PPA
6 tkoi@slac.stanford.eedu 6 tkoi@slac.stanford.eedu
7 7
8 This example shows how to use convergece teste 8 This example shows how to use convergece tester in Geant4.
9 The aim of Convergence Tester 9 The aim of Convergence Tester
10 After a Monte Carlo simulation, we get an answ 10 After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
11 The answer is usually given in a form of avera 11 The answer is usually given in a form of average value.
12 But sometimes the value is strongly affected b 12 But sometimes the value is strongly affected by single or a few events in the full calculation.
13 In such case, we must concern about quality of 13 In such case, we must concern about quality of the value.
14 What we must remember is 14 What we must remember is
15 Large number of history does not valid resul 15 Large number of history does not valid result of simulation.
16 Small Relative Error does not valid result o 16 Small Relative Error does not valid result of simulation
17 Convergence tester provides statistical inform 17 Convergence tester provides statistical information
18 to assist establishing valid confidence interv 18 to assist establishing valid confidence intervals for Monte Carlo results for users.
19 19
20 Geometry and Physics are same to exampleB1. Pl 20 Geometry and Physics are same to exampleB1. Please see README.B1
21 Also run1.mac and run2.mac are like in example <<
22 increased number of events in run1.mac. <<
23 Note that in this example, the classes with th 21 Note that in this example, the classes with the code added for
24 the purpose of demonstration of the Convergenc << 22 the purpose of demonstration of the Convergence Tester start with a prefix
25 B1Con instead of B1 and also the executable an 23 B1Con instead of B1 and also the executable and the test macro names are changed
26 in exampleB1Con and exampleB1Con.in. 24 in exampleB1Con and exampleB1Con.in.
27 25
28 Known problem: 26 Known problem:
29 Computing time of T cannot be gotten properly 27 Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
30 FOM (=1/(R^2T) where R is relative error and T 28 FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
31 29
32 ********************************************** 30 ***********************************************************************************************************************
33 Output example 31 Output example
34 32
35 // Part I.A 33 // Part I.A
36 // Basic statistics values 34 // Basic statistics values
37 35
38 G4ConvergenceTester Output Result of DOSE_TALL 36 G4ConvergenceTester Output Result of DOSE_TALLY
39 EFFICIENCY = 0.601 37 EFFICIENCY = 0.601
40 MEAN = 4.81721e-12 38 MEAN = 4.81721e-12
41 VAR = 2.15334e-23 39 VAR = 2.15334e-23
42 SD = 4.64041e-12 40 SD = 4.64041e-12
43 R = 0.0304622 41 R = 0.0304622
44 SHIFT = 2.22459e-13 42 SHIFT = 2.22459e-13
45 VOV = 0.000166754 43 VOV = 0.000166754
46 FOM = 1238.68 44 FOM = 1238.68
47 45
48 // Part I.B 46 // Part I.B
49 // If the largeset scored events happen at nex 47 // If the largeset scored events happen at next to the last event,
50 // then how much the event effects the statist 48 // then how much the event effects the statistics values of the calculation
51 49
52 THE LARGEST SCORE = 1.07301e-11 and it happe 50 THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
53 Affected Mean = 4.82311e-12 and its rati 51 Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
54 Affected VAR = 2.15468e-23 and its rati 52 Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
55 Affected R = 0.0304192 and its rati 53 Affected R = 0.0304192 and its ratio to orignal is 0.998587
56 Affected SHIFT = 2.1804e-13 and its rati 54 Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
57 Affected FOM = 1238.68 and its rati 55 Affected FOM = 1238.68 and its ratio to orignal is 1
58 56
59 // Part I.C 57 // Part I.C
60 // Convergence tests results 58 // Convergence tests results
61 59
62 MEAN distribution is RANDOM 60 MEAN distribution is RANDOM
63 r follows 1/std::sqrt(N) 61 r follows 1/std::sqrt(N)
64 r is monotonically decrease 62 r is monotonically decrease
65 r is less than 0.1. r = 0.0304622 63 r is less than 0.1. r = 0.0304622
66 VOV follows 1/std::sqrt(N) 64 VOV follows 1/std::sqrt(N)
67 VOV is monotonically decrease 65 VOV is monotonically decrease
68 FOM distribution is not RANDOM 66 FOM distribution is not RANDOM
69 SLOPE is not large enough 67 SLOPE is not large enough
70 This result passes 6 / 8 Convergence Test. 68 This result passes 6 / 8 Convergence Test.
71 69
72 70
73 // Part II 71 // Part II
74 // Profile of statistics values in the history 72 // Profile of statistics values in the history
75 73
76 G4ConvergenceTester Output History of DOSE_TAL 74 G4ConvergenceTester Output History of DOSE_TALLY
77 i/16 till_ith mean var 75 i/16 till_ith mean var sd r vov fom shift e r2eff r2int
78 1 62 4.94618e-12 2.04631e-23 4.52362e 76 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
79 2 124 4.69364e-12 2.10698e-23 4.59018e 77 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
80 3 187 4.72161e-12 2.14009e-23 4.62612e 78 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
81 4 249 4.95617e-12 2.13982e-23 4.62582e 79 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
82 5 312 4.8529e-12 2.13482e-23 4.62041e 80 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
83 6 374 5.14255e-12 2.15736e-23 4.64474e 81 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
84 7 437 5.03849e-12 2.13484e-23 4.62043e 82 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
85 8 499 4.96962e-12 2.1429e-23 4.62914e 83 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
86 9 562 4.91513e-12 2.14709e-23 4.63367e 84 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
87 10 624 4.82995e-12 2.13825e-23 4.62412e 85 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
88 11 687 4.79197e-12 2.13975e-23 4.62574e 86 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
89 12 749 4.77183e-12 2.15116e-23 4.63807e 87 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
90 13 812 4.76087e-12 2.14479e-23 4.63119e 88 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
91 14 874 4.81359e-12 2.13296e-23 4.6184e 89 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
92 15 937 4.82018e-12 2.14558e-23 4.63204e 90 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
93 16 999 4.81721e-12 2.15334e-23 4.64041e 91 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
94 92
95 ********************************************** 93 **************************************************************************************************************************
96 94
97 Reference of this Convergence tests 95 Reference of this Convergence tests
98 MCNP(TM) -A General Monte Carlo N-Particle Tra 96 MCNP(TM) -A General Monte Carlo N-Particle Transport Code
99 Version 4B 97 Version 4B
100 Judith F. Briesmeister, Editor 98 Judith F. Briesmeister, Editor
101 LA-12625-M, Issued: March 1997, UC 705 and UC 99 LA-12625-M, Issued: March 1997, UC 705 and UC 700
102 CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEM 100 CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS
103 VI. ESTIMATION OF THE MONTE CARLO PRECI 101 VI. ESTIMATION OF THE MONTE CARLO PRECISION
104 102