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Geant4/examples/extended/analysis/B1Con/

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File History 2102 bytes       2024-12-05 15:16:16
File README 5790 bytes       2024-12-05 15:16:16
File README.B1 7592 bytes       2024-12-05 15:16:16
File exampleB01Con.out 49607 bytes       2024-12-05 15:16:16
C++ file exampleB1Con.cc 4085 bytes       2024-12-05 15:16:16
File exampleB1Con.in 165 bytes       2024-12-05 15:16:16
File exampleB1Con.out 0 bytes       2024-12-05 15:16:16
File init_vis.mac 373 bytes       2024-12-05 15:16:16
File run1.mac 555 bytes       2024-12-05 15:16:16
File run2.mac 448 bytes       2024-12-05 15:16:16
File vis.mac 3766 bytes       2024-12-05 15:16:16 
  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