March 2007
Leading Particle Biasing
Overview
Jane Tinslay, SLAC
Overview of Techique
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Classic electromagnetic leading particle biasing
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Applications where high energy particles initiate electromagnetic
showers may spend a significant amount of time in analogue
shower simulation
Most important processes contributing to EM shower at high
energies are bremsstrahlung and pair production - ie, two
secondaries produced in each interaction
Reduce computing time by preferentially tracking the highest
energy secondary - highest contribution to energy deposit
Hadronic leading particle biasing
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Hadronic interaction can produce many secondaries of same
type and with similar characteristics
Reduce computing time by discarding a predetermined fraction
of them which don’t significantly contribute to shower
Can also enhance production of interesting secondaries
Jane Tinslay, SLAC
2
Side Effects
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Lateral shower profile not reliably reproduced
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Shower fluctuations not fully modeled
Possible to end up with large weight given to a
few low energy particles
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Energy deposit fluctuations
Codes recommend use of weight windows to control
weight fluctuations
Jane Tinslay, SLAC
3
Applications
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Radiobiological doses
Heating effects
Radiation damage
Estimating shower punch through
Reduce time spend simulating hadronic
cascades
Reduce time spent simulating high energy EM
showers
Jane Tinslay, SLAC
4
Leading Particle Biasing Summary
Classic EM
Hadronic
General
Multiplicity
Tuning
EGS4/EGS5/
EGSnrc
Y
N/A
N
N
Fluka
Y
Y
N
Y
Geant4
N
Y
N
N
MCNP
N
N
N
N
MCNPX
N
Y
Y
Y
Penelope
N
N/A
N
N
Jane Tinslay, SLAC
Multiple
Context
5
EGS4/EGS5/EGSnrc
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EM Leading particle biasing for e-/e+/ initiated showers
When bremsstrahlung/pair production event occurs,
continue to track only one of the two remaining particles
Given:
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R = random number between 0 and 1
F = fraction of kinetic energy assigned to the lower energy
particle:
ELower
F
ELower  EHigher
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If (R < F) keep lower energy particle
If (R> F) keep higher energy particle
I.e, 
preferentially keep higher energy particle, but keep lower
lower energy particle some some of the time, to keep the game
fair
Jane Tinslay, SLAC
6
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Assign surviving particle a weight
ELower  EHigher
W 
ESelected
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Manual states that speed of shower calculations
improved by factor of 300 at 33GeV
Have problems with large weights reducing efficiency
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Generally get factors of 20+
Jane Tinslay, SLAC
7
Fluka EM Leading Particle Biasing
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EM Leading particle biasing for e-/e+/ initiated showers
Derived from the EGS4 implementation
Modified to account for annihilation photons produced
from e+e- annihilation
Secondary particle selection probability proportional to
useful energy rather than kinetic energy
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Useful energy e-/ = KE
Useful energy e+ = KE + 2*me
Selected particle assigned weight which is inverse of
selection probability
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Same as EGS4, with useful energy taken into consideration
Jane Tinslay, SLAC
8
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Supports multiple configurations
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Process combinations:
 Bremsstrahlung and pair production
 Bremsstrahlung
 Pair production
 Positron annihilation at rest
 Compton scattering
 Bhabha & Moller scattering
 Photoelectric effect
 Positron annihilation in flight
Energy thresholds for e-/e+/
Region dependent
Recommend using weight windows to deal with large
weight fluctuations
Jane Tinslay, SLAC
9
Fluka Multiplicity Tuning
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Leading particle biasing for hadrons/muon/photon
photonuclear interactions
Define a factor by which average # secondaries should
be scaled
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Always retain leading particle
If factor < 1, play Russian Roulette to reduce # secondaries
If factor > 1, split secondaries (duplicate particles, split weight)
No Russian Roulette played if # secondaries < 3
Adjust weight as appropriate
Configuration:
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Mixed in with importance sampling configuration
Region by region basis
Possible to apply tuning to primary particles only
Recommend use weight window to control weight fluctuations
(region defined)
Jane Tinslay, SLAC
10
Geant4 Hadronic Leading Particle
Biasing(Current)
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Built in utility for hadronic processes
Keep only the most important part of the event
along with representative tracks of given particle
types
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Always keep leading particle
Of remaining tracks, if a particle type exists, select
one from each of Baryons, 0’s, mesons, leptons
Adjust weight as appropriate
Question: Which frame leading particle determined in
?
Jane Tinslay, SLAC
11
MCNPX Secondary Particle Biasing
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Similar to Fluka multiplicity tuning
Applies to any particle
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Define a factor Sn equivalent to Fluka scale factor
Store appropriate weight
Didn’t see any mention about keeping the leading
particle
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Effectively combined EM/Hadronic leading particle biasing
Possibly implied ?
Supports multiple configurations
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Secondary particle type
Secondary particle energy
Creator particle
Jane Tinslay, SLAC
12
References
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BEAMnrc Users Manual, D.W.O. Rogers et al. NRCC Report PIRS-0509(A)revK (2007)
The EGS4 Code System, W. R. Nelson and H. Hirayama and D.W.O. Rogers, SLAC-265,
Stanford Linear Accelerator Center (1985)
History, overview and recent improvements of EGS4, A.F. Bielajew et al., SLAC-PUB-6499 (1994)
THE EGS5 CODE SYSTEM, Hirayama, Namito, Bielajew, Wilderman, Nelson SLAC-R-730
(2006)
The EGSnrc Code System, I. Kawrakow et al., NRCC Report PIRS-701 (2000)
Variance Reduction Techniques, D.W.O. Rogers and A.F. Bielajew (Monte Carlo Transport of
Electrons and Photons. Editors Nelso, Jankins, Rindi, Nahum, Rogers. 1988)
NRC User Codes for EGSnrc, D.W.O. Rogers, I. Kawrakow, J.P. Seuntjens, B.R.B. Walters and E.
Mainegra-Hing, PIRS-702(revB) (2005)
http://www.fluka.org/course/WebCourse/biasing/P001.html
http://www.fluka.org/manual/Online.shtml
http://geant4.web.cern.ch/geant4/UserDocumentation/UsersGuides/ForApplicationDeveloper/html/
Fundamentals/biasing.html
MCNPX 2.3.0 Users Guide, 2002 (version 2.5.0 is restricted)
PENELOPE-2006: A Code System for Monte Carlo Simulation of Electron and Photon Transport,
Workshop Proceedings Barcelona, Spain 4-7 July 2006, Francesc Salvat, Jose M. FernadezVarea, Josep Sempau, Facultat de Fisica (ECM) , Universitat de Barcelona
Jane Tinslay, SLAC
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