THE EGS5 CODE SYSTEM

4.1.3 Leading Particle Biasing
The second application of importance sampling that we will discuss primarily concerns the deposition
of energy in an electromagnetic shower initiated by a high energy electron (or photon).
The analog approach that is used throughout EGS5, in which each and every particle is generally
followed to completion (ı.e. energy cutoff guarantees that high energy shower calculations will take
lots of time. For the most energetic particle energies under consideration at present day accelerators
(> 50 GeV), one can barely manage to simulate showers in this fashion because of computer
time limitations (Recall that execution time per incident particle grows linearly with energy). Fortunately,
a certain class of problems involving the calculation of energy deposition is well-suited
for a non-analog treatment known as leading particle biasing[175]. Examples of such problems are
radiobiological dose, heating effects, and radiation damage, although some care must be taken in
not being too general with this statement (more on this later).
As a rule, variance reduction techniques of this type should only be used when there is some
prior knowledge of the physical processes that are the most (or least) important to the answer one
is looking for. Leading particle biasing is a classic example of this. The most important processes in
the development of an EM shower, at least in terms of total energy deposition, are bremsstrahlung
and pair production. Furthermore, after every one of these interactions the particle with the higher
of the two energies is expected to contribute most to the total energy deposition.
Leading particle biasing is very easily implemented within the framework of EGS5 by means of
the following statements:
if (iarg.eq.7) then ! Apply Leading Particle Biasing for brems.
eks=e(np)+e(np-1)-RM ! Kinetic energy before brems.
ekenp=e(np)
if(iq(np).ne.0) ekenp=e(np)-RM
call randomset(rnnolp)
if (rnnolp.lt.ekenp/eks) then ! Follow np
e(np-1)=e(np)
iq(np-1)=iq(np)
u(np-1)=u(np)
v(np-1)=v(np)
w(np-1)=w(np)
end if
ekenp=e(np-1)
if (iq(np-1).ne.0) ekenp=e(np-1)-RM
wt(np-1)=wt(np-1)*eks/ekenp
np=np-1
end if
if (iarg.eq.16) then ! Apply Leading Particle Biasing for pair.
eks=e(np)+e(np-1)-2.0*RM
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