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Overview of Leading Particle Biasing Techniques for High Energy Shower Simulations

This overview by Jane Tinslay from SLAC details leading particle biasing techniques for simulating electromagnetic showers initiated by high-energy particles. Emphasis is placed on reducing computational time by tracking the highest energy secondary particles, specifically during bremsstrahlung and pair production processes. The discussion includes hadronic leading particle biasing, its applications in radiation damage estimates, and the effects on shower profiles. Recommendations for managing weight fluctuations are also provided, highlighting practical implementations in various simulation codes.

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Overview of Leading Particle Biasing Techniques for High Energy Shower Simulations

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

  2. 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 Jane Tinslay, SLAC

  3. 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 Jane Tinslay, SLAC

  4. 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 Jane Tinslay, SLAC

  5. Leading Particle Biasing Summary Jane Tinslay, SLAC

  6. 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 Jane Tinslay, SLAC

  7. 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+ Jane Tinslay, SLAC

  8. 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*me • Selected particle assigned weight which is inverse of selection probability • Same as EGS4, with useful energy taken into consideration Jane Tinslay, SLAC

  9. 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 Jane Tinslay, SLAC

  10. 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) Jane Tinslay, SLAC

  11. 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 ? Jane Tinslay, SLAC

  12. 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 Jane Tinslay, SLAC

  13. 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 Jane Tinslay, SLAC

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