| Geant4 Cross Reference |
1 2 Example of Convergence Tester 3 4 Koi, Tatsumi 5 SLAC National Accelerator Laboratory / PPA 6 tkoi@slac.stanford.eedu 7 8 This example shows how to use convergece tester in Geant4. 9 The aim of Convergence Tester 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 average value. 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 the value. 14 What we must remember is 15 Large number of history does not valid result of simulation. 16 Small Relative Error does not valid result of simulation 17 Convergence tester provides statistical information 18 to assist establishing valid confidence intervals for Monte Carlo results for users. 19 20 Geometry and Physics are same to exampleB1. Please see README.B1 21 Also run1.mac and run2.mac are like in exampleB1, with the only diffrence slightly 22 increased number of events in run1.mac. 23 Note that in this example, the classes with the code added for 24 the purpose of demonstration of the Convergence Tester are defined in the namespace 25 B1Con instead of B1 and also the executable and the test macro names are changed 26 in exampleB1Con and exampleB1Con.in. 27 28 Known problem: 29 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 is computing time) relates numbers are unusable. 31 32 *********************************************************************************************************************** 33 Output example 34 35 // Part I.A 36 // Basic statistics values 37 38 G4ConvergenceTester Output Result of DOSE_TALLY 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 next to the last event, 50 // then how much the event effects the statistics values of the calculation 51 52 THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event 53 Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123 54 Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062 55 Affected R = 0.0304192 and its ratio to orignal is 0.998587 56 Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133 57 Affected FOM = 1238.68 and its ratio to orignal is 1 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_TALLY 77 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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-12 0.0304622 0.000166754 1238.68 2.22459e-13 0.601 0.000663894 0.000263125 94 95 ************************************************************************************************************************** 96 97 Reference of this Convergence tests 98 MCNP(TM) -A General Monte Carlo N-Particle Transport Code 99 Version 4B 100 Judith F. Briesmeister, Editor 101 LA-12625-M, Issued: March 1997, UC 705 and UC 700 102 CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS 103 VI. ESTIMATION OF THE MONTE CARLO PRECISION 104