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