text: 
  Example of Convergence Tester 
  
            Koi, Tatsumi
            SLAC National Accelerator Laboratory / PPA
            tkoi@slac.stanford.eedu
  
  This example shows how to use convergece tester in Geant4.
  The aim of Convergence Tester
  After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
  The answer is usually given in a form of average value. 
  But sometimes the value is strongly affected by single or a few events in the full calculation.  
  In such case, we must concern about quality of the value.
  What we must remember is
  	Large number of history does not valid result of simulation.
  	Small Relative Error does not valid result of simulation
  Convergence tester provides statistical information 
  to assist establishing valid confidence intervals for Monte Carlo results for users.
  
  Geometry and Physics are same to exampleB1. Please see README.B1
  Note that in this example, the classes with the code added for
  the purpose of demonstration of the Convergence Tester start with a prefix
  B1Con instead of B1 and also the executable and the test macro names are changed
  in exampleB1Con and exampleB1Con.in. 
  
  Known problem:
  Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore  
  FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
  
  ***********************************************************************************************************************
  Output example
  
  // Part I.A
  // Basic statistics values 
  
  G4ConvergenceTester Output Result of DOSE_TALLY
         EFFICIENCY =         0.601
               MEAN =   4.81721e-12
                VAR =   2.15334e-23
                 SD =   4.64041e-12
                  R =     0.0304622
              SHIFT =   2.22459e-13
                VOV =   0.000166754
                FOM =       1238.68
  
  // Part I.B
  // If the largeset scored events happen at next to the last event, 
  // then how much the event effects the statistics values of the calculation
  
  THE LARGEST SCORE =   1.07301e-11 and it happend at 487th event
      Affected Mean =   4.82311e-12 and its ratio to orignal is 1.00123
       Affected VAR =   2.15468e-23 and its ratio to orignal is 1.00062
         Affected R =     0.0304192 and its ratio to orignal is 0.998587
     Affected SHIFT =    2.1804e-13 and its ratio to orignal is 0.980133
       Affected FOM =       1238.68 and its ratio to orignal is 1
  
  // Part I.C
  // Convergence tests results
  
  MEAN distribution is  RANDOM
  r follows 1/std::sqrt(N)
  r is monotonically decrease 
  r is less than 0.1. r = 0.0304622
  VOV follows 1/std::sqrt(N)
  VOV is monotonically decrease 
  FOM distribution is not RANDOM
  SLOPE is not large enough
  This result passes 6 / 8 Convergence Test.
  
  
  // Part II
  // Profile of statistics values in the history 
  
  G4ConvergenceTester Output History of DOSE_TALLY
  i/16 till_ith      mean          var           sd            r          vov          fom        shift            e        r2eff        r2int
     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
     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
     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
     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
     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
     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
     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
     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
     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
    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
    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
    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
    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
    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
    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
    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
  
  **************************************************************************************************************************
  
  Reference of this Convergence tests
  MCNP(TM) -A General Monte Carlo N-Particle Transport Code
  Version 4B
  Judith F. Briesmeister, Editor
  LA-12625-M, Issued: March 1997, UC 705 and UC 700
  CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS
         VI. ESTIMATION OF THE MONTE CARLO PRECISION
  
  