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An exact framework for Uncertainty Quantification in Monte Carlo simulation

An exact framework for Uncertainty Quantification in Monte Carlo simulation. Paolo Saracco , Maria Grazia Pia INFN Genova , Italy. CHEP 2013 Amsterdam, 14-18 October 2013. Monte Carlo in HEP . c ross sections, b ranching ratios, p hysics models, p hysics parameters. input. MC

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An exact framework for Uncertainty Quantification in Monte Carlo simulation

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  1. An exact framework for Uncertainty Quantification in Monte Carlo simulation Paolo Saracco, Maria Grazia Pia INFN Genova, Italy CHEP 2013 Amsterdam, 14-18 October 2013

  2. Monte Carlo in HEP cross sections, branching ratios, physics models, physics parameters.. input MC engine observable Event generator (Pythia, Herwig…) Particle transport (Geant4, MCNP, MARS…) primary particles, deposited energy… How much can we trust the observables produced by MC?

  3. Measure and compare Measured and simulated observable Test beam Courtesy CERN CDS, CERN-EX-0305054 If the accuracy of observable A is assessed by comparison with experiment, what about observable B? And what about the simulation of detector concepts, which do not exist yet? And observables which cannot be measured in practice?

  4. Uncertainties Monte Carlo method input with uncertaintiesA Validation of MC modeling ingredients observable with uncertainties • Uncertainties deriving from • input uncertainties • Monte Carlo algorithm • simulation model Beware: input uncertainties can be hidden in models and algorithms in the code Uncertainty quantification is the ground for predictive Monte Carlo simulation

  5. Parameter uncertainties They are the uncertainties of the “ingredients” of the simulation engine (particle transport system, event generator) Can they be disentangled from statistical uncertainties associated with the Monte Carlo method? Can we estimate their effect on the observables produced by the simulation? Sensitivity analysis Exact calculation

  6. Disentangling uncertainties Algorithmic (statistical) and parameter uncertainties

  7. Sensitivity analysis Computational cost • Mathematical methods • Software toolkits (DAKOTA, PSUADE..)

  8. Uncertainty propagation Spiegareilconcetto

  9. Paolo’s slides go here

  10. Current limitations Bisognaspiegarequalisiano le attualilimitazioni (concettuali e pratiche) del “nostro” UQ, e come intendiamoaffrontarle

  11. Validation of physics ingredients • The validation of the physics “ingredients” of Monte Carlo codes is a (complex, slow) still ongoing process • at least regarding Monte Carlo particle transport • Quantitative estimates of input uncertainties (cross sections, angular distributions, BR etc.) are necessary for uncertainty propagation • A qualitative plot is not enough… • Epistemic uncertainties are often embedded in the code, without being documented • See M. G. Pia, M. Begalli, A. Lechner, L. Quintieri, P. Saracco, Physics-related epistemic uncertainties of proton depth dose simulation, IEEE Trans. Nucl. Sci., vol. 57, no. 5, pp. 2805-2830, 2010

  12. Conclusion and outlook Collaborative contribution from HEP experiments is welcome!

  13. Collezione di immaginiutili

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