1. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 Event Biasing in G4: Why and what? Key User Requirements J. Apostolakis, CERN 19 March 2007 With thanks to many others for slides borrowed from their talks

2. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 Overview Key use cases Different options event biasing Comparing features –Established tools vs. Geant4

3. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 Key biasing use cases Shielding –By design few particles penetrate a shield Calculating their effect, with suppression factors like 10 6 requires specialized techniques Radiation environment –The experimental hall, an underground laboratory, a labyrinth Background calculations –Understanding experiment’s backgrounds

4. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 What is biasing  Analog simulation: the possible outcomes of measurements to the estimator of an observable occur with the same frequencies – ‘as they do in nature’  All possible events are sampled with the same frequency  Biased simulation: important contributions to the estimator are sampled more often than the less important ones.  More important events are sampled more often.  The variance reduction techniques (VRT) aim to reduce the computing time, maintaining the mean value of an estimator and reducing its variance.

5. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004  Four classes of VRT are:  population control methods (geometry splitting and Russian roulette, energy splitting/roulette, weight cutoff, weight window): many samples of low weight are tracked in important regions, while few samples of high weight in unimportant regions  truncation methods (energy and time cutoff): like a Russian roulette game with zero survival probability  Four types of VRT

6. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004  Partially deterministic methods (next event estimators, controlling the random number sequence):  control the (normally) random walk process through deterministic-like sequence, i.e. DXTRAN in MCNP  Modified sampling methods (exponential transform, implicit capture, forced collisions, source biasing):  sample from a different distribution rather than the physical probability  as long as the particle weights are then adjusted to compensate Types of VRT (cont).

7. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 Event biasing techniques in Geant4  Event biasing is used in many application areas such as:  radiation shielding  dosimetry  Geant4 is a toolkit and all source code is open  the user can implement his/her own method  CMS, ESA, Alice and other experiments have already their own implementations of event biasing options  It’s convenient for the user that Geant4 itself provides most commonly used event biasing techniques.

8. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 TalliesMCNPXGEANT4FLUKAMARSPHITS Standard Flux Volume Surface Point/ring Current Charge Kinetic energy Particle density Reaction rates Energy dep. Rapidity DPA Momentum Pulse-height Termination Modifiers Yes No Some No Yes Partial 10 Yes Limited No Limited Yes No Yes ?? Yes User input ?? 3 Yes No Yes No Star (inelastic) Yes ?? 2 Yes Yes (neutrons) Yes No Yes Yes. ?? Yes No ?? 2 Yes No Yes No Yes No ?? No Yes 2 Special Mesh Coincidence Residuals Event logs rec, cyl, sph Yes rec, cyl No Yes rec, cyl Yes rec, cyl, sph Yes rec,cyl Yes Convergence Tests10Error Review of Monte Carlo All-Particle Transport Codes and.. G. W. McKinney (Los Alamos National Laboratory) et al, FNDA 2006, Cape Town, South Africa, April 3-6, 2006

9. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 TalliesMCNPXGEANT4FLUKAMARSPHITS ViewerBuilt-in: 1-D, 2-D Contour External: IDL Tecplot GNUplot PAW Built-in: No External: JAS PI Open Scientist Built-in: No External: PAW ROOT Built-in: Custom External: PAW Built-in: Angel External: Angel Variance Reduction Population control Region biasing Weight cutoff Weight window mesh Energy biasing Yes No Yes No Yes Modified sampling Source biasing Implicit capture Exp. transform Production biasing Yes RDM Yes No Yes No Yes No Yes DXTRANYesNo Viewer2-D contourNo

10. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004 Review of Monte Carlo All-Particle Transport Codes and Overview of Recent MCNPX Features G. W. McKinney J. W. Durkee, J. S. Hendricks, M. R. James, D. B. Pelowitz, L. S. Waters Los Alamos National Laboratory Los Alamos, NM F. X. Gallmeier Oak Ridge National Laboratory Oak Ridge, TN FNDA 2006, Cape Town, South Africa, April 3-6, 2006

11. Adapted from G G Daquino, Geant4 Workshop, Catania, 5 Oct 2004