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Maria Grazia Pia, INFN Genova Epistemic and systematic uncertainties in Monte Carlo simulation: Epistemic and systematic uncertainties in Monte Carlo simulation: an investigation in proton Bragg peak simulation Maria Grazia Pia INFN Genova, Italy Maria Grazia Pia 1, Marcia Begalli 2, Anton Lechner 3, Lina Quintieri 4, Paolo Saracco 1 1 INFN Sezione di Genova, Italy 2 State University Rio de Janeiro, Brazil 3 Vienna University of Technology, Austria 4 INFN Laboratori Nazionali di Frascati,, Italy SNA + MC 2010 Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2010

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Maria Grazia Pia, INFN Genova Quantifying the unknown in Monte Carlo simulation Maria Grazia Pia INFN Genova, Italy Maria Grazia Pia 1, Marcia Begalli 2, Anton Lechner 3, Lina Quintieri 4, Paolo Saracco 1 1 INFN Sezione di Genova, Italy 2 State University Rio de Janeiro, Brazil 3 Vienna University of Technology, Austria 4 INFN Laboratori Nazionali di Frascati,, Italy SNA + MC 2010 Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2010

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Maria Grazia Pia, INFN Genova Epistemic uncertainties Possible sources Possible sources in Monte Carlo simulation incomplete understanding of fundamental physics processes, or practical inability to treat them thoroughly non-existent or conflicting experimental data for a physical parameter or model applying a physics model beyond the experimental conditions in which its validity has been demonstrated Epistemic uncertainties originate from lack of knowledge Epistemic uncertainties affect the reliability of simulation results Can we quantify them? Relatively scarce attention so far in Monte Carlo simulation Studies in deterministic simulation (especially for critical applications)

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Maria Grazia Pia, INFN Genova Uncertainty quantification Epistemic uncertainties are difficult to quantify due to their intrinsic nature No generally accepted method of measuring epistemic uncertainties and their contributions to reliability estimation Various formalisms developed in the field of deterministic simulation Interval analysis Interval analysis Dempster-Shafer theory of evidence Dempster-Shafer theory of evidence Not always directly applicable in Monte Carlo simulation Adapt, reinterpret, reformulate existing formalisms Develop new ones specific to Monte Carlo simulation

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Maria Grazia Pia, INFN Genova Benefits of quantifying uncertainties Epistemic uncertainties are reducible Can be reduced or suppressed by extending knowledge New experimental measurements Uncertainty quantification gives us guidance about What to measure What experimental precision is needed/adequate Priorities: which uncertainties generate the worst systematic effects Measurements are not always practically possible Uncertainty quantification to control systematics

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Maria Grazia Pia, INFN Genova Warm-up exercise Epistemic uncertainties quantification in proton depth dose simulation simplicity complexity

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Maria Grazia Pia, INFN Genova Ingredients p stopping powers Water ionisation potential -ray production Multiple scattering Nuclear elastic Nuclear inelastic Cross sections Preequilibrium Deexcitation Intranuclear cascade EGS5, EGSnrc Penelope MCNP(X) PHITS SHIELD-HIT FLUKA GEANT 3 SPAR, CALOR, CEM, LAHET, INUCL, GHEISHA, Liège INCL, Bertini -ray or no -ray Preequilibrium or no preequilibrium Weisskopf-Ewing or Weisskopf-Ewing Griffin-exciton or hybrid etc.

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Maria Grazia Pia, INFN Genova Geant4 physics options Water ionization potential set through the public interface of G4Material

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Maria Grazia Pia, INFN Genova Validation in the literature Beam energy (and energy spread) is not usually known with adequate precision in therapeutical beam lines What matters in clinical applications is the range Typical procedure: optimize the beam parameters to be used in the simulation by fitting them to experimental data Determine beam energy, energy spread etc. Use optimized beam parameter values in the simulation This is a calibration This is NOT validation T. G. Trucano, L. P. Swiler, T. Igusa, W. L. Oberkampf, and M. Pilch, Calibration, validation, and sensitivity analysis: Whats what, Reliab. Eng. Syst. Safety, vol. 91, no , pp , 2006.

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Maria Grazia Pia, INFN Genova Simulation environment Realistic proton beam line Geometry from Geant4 hadrontherapy advanced example G. A. P. Cirrone, G. Cuttone, S. Guatelli, S. Lo Nigro, B. Mascialino, M. G. Pia, L. Raffaele, G. Russo, M. G. Sabini,Implementation of a New Monte Carlo GEANT4 Simulation Tool for the Development of a Proton Therapy Beam Line and Verification of the Related Dose Distributions, IEEE Trans. Nucl. Sci., vol. 52, no. 1, pp , 2005 Water sensitive volume, longitudinal 200 m slices (through G4ReadoutGeometry) Proton beam: E = MeV, E = 300 keV Physics modeling options configured through an application design based on G4VModularPhysicsList 1 million primary protons Geant4 8.1p02, 9.1(ref-04), 9.2p03, 9.3

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Maria Grazia Pia, INFN Genova Reference physics configuration Wellisch & Axen

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Maria Grazia Pia, INFN Genova General features electromagnetic electromagnetic + hadronic elastic electromagnetic + hadronic elastic + hadronic inelastic electrons MeV peak =376 keV

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Maria Grazia Pia, INFN Genova Water mean ionisation potential E p = MeV I = 75 eV, 67.2 eV, 80.8 eV E p = MeV (1 from MeV) I = 80.8 eV GoF tests Bragg-Bragg p-value = 1 (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises)

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Maria Grazia Pia, INFN Genova Proton stopping powers ICRU49 Ziegler77 Ziegler85 Ziegler2000 Differences would be masked by typical calibration of simulation input parameters

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Maria Grazia Pia, INFN Genova Hadronic elastic scattering U-elastic Bertini-elastic LEP (GHEISHA-like) CHIPS-elastic p-value (reference: U-elastic) Bertini LEP CHIPS Wald-Wolfowitz test: p-value< Difference of deposited energy in longitudinal slices

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Maria Grazia Pia, INFN Genova Hadronic inelastic cross sections GHEISHA-like Wellisch & Axen Difference of deposited energy in longitudinal slices 99% confidence interval for inelastic scattering occurrences in water (Wellisch & Axen cross sections): Occurrences with GHEISHA-like cross sections: 1654 Bragg peak profiles p-value > 0.9 (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises)

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Maria Grazia Pia, INFN Genova Hadronic inelastic scattering models No visible difference in Bragg peak profiles Wald-Wolfowitz test p-value< for all model options except p-value=0.360 for Liège cascade p-value (reference: Precompound) preequilibrium = no preequilibrium

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Maria Grazia Pia, INFN Genova Hadronic inelastic differences Difference of deposited energy in longitudinal slices Bertini LEP Liège CHIPS reference: Precompound secondary p secondary n Precompound Bertini LEP Liège CHIPS Precompound Bertini LEP Liège CHIPS Wald-Wolfowitz test: p-value < 0.001

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Maria Grazia Pia, INFN Genova Nuclear deexcitation reference: default Evaporation GEM evaporation Fermi break-up Difference of deposited energy in longitudinal slices Geant4 < 9.3 (bug fix) default evaporation GEM evaporation Fermi break up Binary Cascade

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Maria Grazia Pia, INFN Genova Cascade-preequilibrium Precompound model activated through Binary Cascade w.r.t. standalone Precompound model Difference of deposited energy in longitudinal slices systematic effect In Geant4 Binary Cascade model cascading continues as long as there are particles above a 70 MeV kinetic energy threshold (along with other conditions required by the algorithm) Transition between intranuclear cascade and preequilibrium determined by empirical considerations

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Maria Grazia Pia, INFN Genova Some get lost on the way… 95% confidence intervals July 2006 December 2009 Calibration: 50 and 200 GeV

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Maria Grazia Pia, INFN Genova Multiple scattering RangeFactorStepLimitLatDisplacementskingeomFactorModel UrbanMSC UrbanMSC 9.2p UrbanMSC UrbanMsc hMS UrbanMsc90 G4MultipleScattering G4hMultipleScattering G4hMultipleScattering, Geant4 9.3 G4MultipleScattering, Geant4 9.3 G4MultipleScattering, Geant4 9.2p03 G4MultipleScattering, Geant4 9.1 G4MultipleScattering, Geant4 8.1p02 Reference: Geant4 9.3 G4hMultipleScattering Difference: G4MultipleScattering in Geant p03 8.1p02 Difference of deposited energy in longitudinal slices

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Maria Grazia Pia, INFN Genova Goodness-of-fit

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Acceptance 99.9% CI 9.3 hMS p p02 Total deposited energy p p hMS 99.9% CI 8.1p hMS

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Maria Grazia Pia, INFN Genova IEEE Trans. Nucl. Sci., vol. 57, no. 5, pp , October 2010 M.G.Pia, M. Begalli, A. Lechner, L. Quintieri, P. Saracco Physics-related epistemic uncertainties in proton depth dose simulation fresh from the oven…

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Maria Grazia Pia, INFN Genova Conclusions Evaluation of systematic effects associated with epistemic uncertainties Sensitivity analysis (~interval analysis) More refined methods: Dempster-Shafer Methods specific to Monte Carlo simulation? Complementary statistical methods contribute to identify and quantify effects Qualitative appraisal is not adequate Epistemic uncertainties are reducible Can be reduced or suppressed by extending knowledge New experimental measurements Uncertainty quantification gives us guidance about What to measure What experimental precision is needed/adequate Priorities: which uncertainties generate the worst systematic effects The impact of epistemic uncertainties depends on the experimental application environment

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Maria Grazia Pia, INFN Genova Backup Geant4/examples/extended/electromagnetic/testEm5/mumsc/deviation.ascii

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