1. Leading Particle Biasing Overview Jane Tinslay, SLAC March 2007

2. Jane Tinslay, SLAC2 Overview of Techique Classic electromagnetic leading particle biasing 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 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

3. Jane Tinslay, SLAC3 Side Effects Lateral shower profile not reliably reproduced Shower fluctuations not fully modeled Possible to end up with large weight given to a few low energy particles Energy deposit fluctuations Codes recommend use of weight windows to control weight fluctuations

4. Jane Tinslay, SLAC4 Applications Radiobiological doses Heating effects Radiation damage Estimating shower punch through Reduce time spend simulating hadronic cascades Reduce time spent simulating high energy EM showers

5. Jane Tinslay, SLAC5 Leading Particle Biasing Summary Classic EMHadronic General Multiplicity Tuning Multiple Context EGS4/EGS5/ EGSnrc YN/ANN FlukaYYNY Geant4NYNN MCNPNNNN MCNPXNYYY PenelopeNN/ANN

6. Jane Tinslay, SLAC6 EGS4/EGS5/EGSnrc 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: R = random number between 0 and 1 F = fraction of kinetic energy assigned to the lower energy particle: 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

7. Jane Tinslay, SLAC7 Assign surviving particle a weight Manual states that speed of shower calculations improved by factor of 300 at 33GeV Have problems with large weights reducing efficiency Generally get factors of 20+

8. Jane Tinslay, SLAC8 Fluka EM Leading Particle Biasing 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 Useful energy e - /  = KE Useful energy e+ = KE + 2*m e Selected particle assigned weight which is inverse of selection probability Same as EGS4, with useful energy taken into consideration

9. Jane Tinslay, SLAC9 Supports multiple configurations 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

10. Jane Tinslay, SLAC10 Fluka Multiplicity Tuning Leading particle biasing for hadrons/muon/photon photonuclear interactions Define a factor by which average # secondaries should be scaled 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: 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)

11. Jane Tinslay, SLAC11 Geant4 Hadronic Leading Particle Biasing(Current) Built in utility for hadronic processes Keep only the most important part of the event along with representative tracks of given particle types 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 ?

12. Jane Tinslay, SLAC12 MCNPX Secondary Particle Biasing Similar to Fluka multiplicity tuning Applies to any particle Effectively combined EM/Hadronic leading particle biasing Define a factor Sn equivalent to Fluka scale factor Store appropriate weight Didn’t see any mention about keeping the leading particle Possibly implied ? Supports multiple configurations Secondary particle type Secondary particle energy Creator particle

13. Jane Tinslay, SLAC13 References 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. Fernadez- Varea, Josep Sempau, Facultat de Fisica (ECM), Universitat de Barcelona