THE EGS5 CODE SYSTEM

4.1.3 Leading Particle BiasingThe second application of importance sampling that we will discuss primarily concerns the depositionof 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 generallyfollowed to completion (ı.e. energy cutoff guarantees that high energy shower calculations will takelots 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 computertime 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-suitedfor a non-analog treatment known as leading particle biasing[175]. Examples of such problems areradiobiological dose, heating effects, and radiation damage, although some care must be taken innot 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 someprior knowledge of the physical processes that are the most (or least) important to the answer oneis looking for. Leading particle biasing is a classic example of this. The most important processes inthe development of an EM shower, at least in terms of total energy deposition, are bremsstrahlungand pair production. Furthermore, after every one of these interactions the particle with the higherof 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 ofthe 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)-RMcall randomset(rnnolp)if (rnnolp.lt.ekenp/eks) then ! Follow npe(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 ifekenp=e(np-1)if (iq(np-1).ne.0) ekenp=e(np-1)-RMwt(np-1)=wt(np-1)*eks/ekenpnp=np-1end ifif (iarg.eq.16) then ! Apply Leading Particle Biasing for pair.eks=e(np)+e(np-1)-2.0*RM191