1. Automated Electron Step Size Optimization in EGS5 Scott Wilderman Department of Nuclear Engineering and Radiological Sciences, University of Michigan

2. Multiple Scattering Step Sizes in Monte Carlo Electron Transport Why is there a dependence? Transport mechanics Optimal step: longest steps that get “right” answer “Right” answer depends on: –Particular problem tallies -- “granularity” –Error tolerance EGS5 automated method –Broomstick problem –Energy hinge –Initial step size restrictions

3. Condensed History Transport Mechanics

4. Why? Larsen convergence: with small enough steps, should get right answer But speed requires long steps, and step lengths limited by accuracy of transport mechanics model Anyone can get  trick is f(x,y,z), and the best we can do is preserve averages (moments) Even with perfect f(x,y,z), there will be a step-size dependence for any tally that is a function of what’s happening along the actual track

5. Problem Granularity Dependence of Step Size

6. EGS5 Step Size Parameters Dual Hinge implies two step size controls, one for multiple scattering, and one for energy loss EGS5(  used fractional energy loss to set steps: –ESTEPE for energy loss hinge –EFRACH for multiple scattering hinge But had both high E and low E values for each hinge variable – 4 different ESTEPES !

7. Results: Backscatter and Timing Run%BackscatterCPU Time Solid.115842 603 01/01/01/01.115842 632 01/40/01/01.1146 244 01/40/01/40.1135 91 01/40/40/40.1306 80 40/40/01/40.1269 38 40/40/40/40.1529 26

8. Central Axis Depth Dose

9. How to Proceed? Accuracy depends on problem “granularity” –Long steps okay for “bulk” volume tallies –Short steps needed for fine mesh computations Speed requires energy dependent step sizes: –Small fractional energy loss at high E for accuracy –Larger fractional loss at low E for speed Base step sizes on some measure of problem geometry granularity (“characteristic dimension”) that can be energy dependent -- solve “broomstick problem”

10. Broomstick Problem

11. Very sensitive to step size -- infinitesimally small broomstick, step must be 1 elastic mfp Determine longest average hinge step which preserves correct average track for given diameter (characteristic dimension) Measure tracklength as energy deposition Measure hinge steps as scattering strength

12. Broomstick Methodology Run EGS5 on broomstick problem for range of Z, E, hinge sizes vs. broomstick diameters t Determine max hinge step (K_1) for 1% energy deposition convergence vs. Z, E, t K_1 varies roughly as t  Z (Z + 1) / A Interpolate distance in terms of (t  Interpolate materials in Z (Z + 1) / A

13. Broomstick Elements

14. Broomstick Parameters Energy range: at.1,.2,.3,.5,..17 in every decade from 2 keV to 1 TeV Broomstick space: dimensions in terms of fraction of CSDA range at.1,.2,.3,.5,.7 in every decade from 1E-6 to.50 Hinge step space: steps in terms of fractional energy loss at.1,.15,.2,.3,.5,.7 in every decade from 1E-4 to.30

15. Broomstick Results

16. Broomstick Drawbacks Broomstick L = CSDA range, so long run times, limiting to 50k histories Little scattering at high energies, so significant fraction of energy deposition occurs before step sizes are important Net effect: Step size optimization criteria based on 1% converged energy deposition not stringent enough

17. Modified Broomstick

18. Set broomstick length = diameter Look at emerging from end Shorter volumes permit more histories 1% convergence in clearly more strict criteria than 1% convergence in May be slower than necessary on some problems, but better accuracy on all problems

19. Modified Broomstick Results

20. Determine maximum fractional energy loss for convergence to 1% in vs. t for all Z and E Convert from EFRACH to K_1 Perform linear fit of log(K_1) vs. log(t), all Z and E New EGS5 subroutine RK1 prepares K_1(E) for all materials, given input t.

21. Modified Broomstick Results

22. Tutor4 with EGS5 2 MeV electrons on 2 mm of Si Reflected ETransmitted E EGS4 default1.3%49.2% EGS4 1% ESTEPE6.4%61.3% EGS5 30% EFRACH8.1%66.5% EGS5 1% EFRACH7.3%64.4% EGS5 2 mm charD7.4%64.8%

23. Energy Hinge E_0 Energy hinge h t E_1 Mono-energetic transport between energy hinges Hinges needed only for accuracy of f(E_0) variables

24. Energy Hinge EGS5 integrals: f(E_0) h + f(E_1) (t – h)  h uniformly distributed in  E:  E / SP(E_0) All Monte Carlo programs must deal with energy dependence over steps. EGS5 relies on average values to be correct.  E (f(E_0) + f(E_1)) / 2 Can show EGS5

25. Energy Hinge PEGS5 compute ESTEPE (E) such that trapezoid rule accurate to within some  current default) Checks stopping power (for energy loss) Checks scattering power (for multiple scattering strength) Checks on hard collision cross section, mean free path not yet implemented Typical values for ESTEPE : between 2% and 8%

26. First Step Artifacts EGS4  EGS5  Gamma angle correlated to electron angle after scatter Gamma angle correlated to electron angle before scatter

27. EGS5 First Step for Primary Electrons Interface usual EGS5 first step, as determined from K_1(Z,E,t) incident electron limited first step, K_f, determined from K_1(Z,E,t_min) 2 K_f4 K_f8 K_f min(16 K_f, K_1(Z,E,t))

28. Summary Optimal step selection will always depend on the problem tally granularity, and in particular, on the importance of events taking place on the first step The new method for setting step sizes in EGS5 based on the “characteristic dimension” of the tally regions usually solves this problem for the user