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Basics of Nuclear Data Evaluation and Perspectives

Basics of Nuclear Data Evaluation and Perspectives. H. Leeb Atominstitut,TU Wien, Austria. Research at the Atominstitut. atomic physics , quantum optics (J. Schmiedmayer ). radiation physics ( Ch . Streli ). low-temperature physics , Super conductivity (H. Weber).

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Basics of Nuclear Data Evaluation and Perspectives

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  1. Basics ofNuclear Data Evaluation andPerspectives H. Leeb Atominstitut,TU Wien, Austria NuPECCMeeting,Vienna, March 13, 2009

  2. Research atthe Atominstitut atomicphysics, quantumoptics (J. Schmiedmayer) radiationphysics (Ch. Streli) low-temperaturephysics, Super conductivity (H. Weber) appliedquantumphysics (N.N.) neutronandquantumphysics (H. Abele) nuclearandparticlephysics (H. Leeb) NuPECC Meeting,Vienna, March 13, 2009

  3. NuclearandParticlePhysics NuclearPhysicsandNuclearAstrophysics(H. Leeb) scatteringandreactiontheory, nucleardataevaluation HadronPhysicsand Fundamental Interactions (M.Faber, H. Markum) exoticatoms, latticegaugetheory Experimental ParticlePhysics(Ch. Fabjan) detectordevelopments, dataanalysistechniques directlylinkedtothe Institute of High EnergyPhysics ofthe Austrian AcademyofSciences NuPECC Meeting,Vienna, March 13, 2009

  4. NuclearPhysicsandNuclearAstrophysics Theoreticaldescriptionofscatteringandreactionprocesses andtheinterpretationof observables withregardtointeractions andunderlyingstructures in basicandappliedphysics Scatteringandreactiontheory • inverse scatteringtechniques • opticalpotentialsandspecificreactions • phaseproblem in quantummechanics Neutron-inducedreactions • nucleardataevaluation • nuclearastrophysics involvement in theexperiments atn_TOF@CERNand in Geel NuPECC Meeting,Vienna, March 13, 2009

  5. Experiments: n-inducedcrosssections n_TOF@CERN (n,g) crosssectionsfortransmutationandastrophysics GELINA (JRC) (n,2n) crosssections via prompt g-decay Experiments performedwithincollaboration: TU Wien and University of Vienna G. Badurek, E. Jericha, H. Leeb, A. Pavlik, A. Wallner NuPECC Meeting,Vienna, March 13, 2009

  6. (n,xn) crosssections E. Jericha (TU Wien) A. Pavlik (Univ. Wien) GELINA (JRC) 209Bi(n,2n) crosssections Measurement of prompt g-raysofthe residual nucleus (even A) 4+ 2 + 0 + Mihailescu et al. ND2007 NuPECC Meeting,Vienna, March 13, 2009

  7. (n,g) crosssections n_TOF@CERN (n,g) (n,f) 4p total absorption calorimeter (TAC) astrophysicalrelevance s-process mainresponsibilityof TU Wien: proper uncertaintyanalysis NuPECC Meeting,Vienna, March 13, 2009

  8. Experimental uncertaintiesatn_TOF 232Th(n,g) 151Sm(n,g) E‘ MeV E MeV 151Sm(n,g) 232Th(n,g) E‘ MeV E‘ MeV normalizedcovariancematrixofthen_TOFexperiment NuPECC Meeting,Vienna, March 13, 2009

  9. Nucleardataevaluation Start of Modern Data Evaluation: recommendedvaluesof fundamental physicsconstants (c, h, af, ... ) Dunnington (1939); Du Mond and Cohen (1953) Present Status: AtpresentEvaluatedNuclear Data Files represent a consistentsetof crosssectionsandassociatedquantitiesfor all relevant reaction processes. Most datafilesare limited totheenergyregionbelow 20MeV. Thereexistseveralnucleardatalibrarieswithevaluatedcrosssection data, but onlyfewfilescontainuncertaintyinformation thereliability Is still an open question. JEFF3.1, ENDF/B-VII, JENDL, CENDL, … NuPECC Meeting,Vienna, March 13, 2009

  10. Conceptofevaluation Nucleardataevaluationisessentially a procedurefollowing therulesofBayesianstatisticswithin a subjectiveinterpretationtheprobabilityreflectsourexpectation no experimental verification Evaluation isgiven in termsof - expectationvaluesof observables- covariancematricesof observables (crosssections) BAYESIAN STATISTICS NuPECC Meeting,Vienna, March 13, 2009

  11. Bayestheorem Bayes Theorem (1763): p(x|s M) = p(s |xM) p(x|M) / p(s |M) posterior = likelihood x prior / evidence x ... model parameter s ... data M ... other information from experiment Choice of proper prior ? Expectationvalue: Covariancematrixelement: NuPECC Meeting,Vienna, March 13, 2009

  12. Evaluations donebyVonach et al. First evaluations in thefieldofnucleardatewhichincludeuncertaintieswereperformedbyVonach et al. (Univ. Vienna) about 1990 Theyconsiderednucleiwheremany experimental datahavebeenavailable  choiceofprior not essential S. Tagesen, H. Vonach, A. Wallner, ND2007 NuPECC Meeting,Vienna, March 13, 2009

  13. Developments in nucleardataevaluation • CurrentDemands: • Inclusionofuncertaintyinformation covariancematrices • Extension ofenergyrangeto ~150MeV • Challenges: • Evaluation processandcovariancematrices – scarcityof experimental datafor E > 20 MeV quest ofuncertaintyofnuclearmodels • Improvementofmodels: nuclearreactions, fission, … NuPECC Meeting,Vienna, March 13, 2009

  14. Bayestheorem Bayes Theorem (1763): p(x|s M) = p(s |xM) p(x|M) / p(s |M) posterior = likelihood x prior / evidence x ... model parameter s ... data M ... other information from experiment Choice of proper prior ? Expectationvalue: Covariancematrixelement: NuPECC Meeting,Vienna, March 13, 2009

  15. Choice of proper prior GOAL quantitative estimateofthereliabilityofnuclear model basedevaluations • Define an almostunbiasedprior • Accountfor all aprioriknowledge • Minimal useof experimental data NuPECC Meeting,Vienna, March 13, 2009

  16. Sourcesofuncertainties The contributions to the covariance matrix of the model are M(mod) = M(par) + M(num) + M(def) parameter uncertainties Model defects non-statistical error numerical implementation error EFFDOC-1047 NuPECC Meeting,Vienna, March 13, 2009

  17. Parameter uncertainties For most cases where there is no obvious prior Baye proposed to apply Laplace principle of insufficient reasoning, i.e. a uniform distribution Main criticism from objectivist: the choice of prior is arbitrary!!! INFORMATION THEORY(Shannon 1949) Information entropy: The amount of uncertainty is maximal if the entropy is maximal. Assumption: Besides the marginalisation we know an expection value NuPECC Meeting,Vienna, March 13, 2009

  18. Theoryforpriordetermination Principleof maximal informationentropy Information Entropy Constraints Determination of Lagrange par. l prior partition function variance Invariant measure to account for continuous parameters: for scaling parameters: NuPECC Meeting,Vienna, March 13, 2009

  19. Admissiblerangeofparameters dependence on av of admissible range in rv admissible range in av z defines lower boundary NuPECC Meeting,Vienna, March 13, 2009

  20. Parameter distributionfor208Pb potential parameters rv(fm) v1(MeV) NuPECC Meeting,Vienna, March 13, 2009

  21. Parameter uncertainties-correlations stotal selastic phenomenologicalopticalpotentials microscopicopticalpotentials NuPECC Meeting,Vienna, March 13, 2009

  22. Model defects - scaling Global scaling factor for each reaction channel c Mean value and vairance for each energy bin Em and isotope n This coarse approximation provides a covariance matrix PROBLEM: not statistically defined NuPECC Meeting,Vienna, March 13, 2009

  23. Model defectsof16O Example16O total crosssection experimental datafor 12C,14N,19F,20Ne,23Na,24Mg E MeV E‘ MeV 0 30% relative variance in % E MeV 20% 60 10 60 NuPECC Meeting,Vienna, March 13, 2009

  24. Correlations - comparison correlationsof total crosssection uncertainties16O cut: E+E‘=const 0.6 0.0 complete prior E MeV 60 10 60 more details in Final report of EFDA-TW6-TTMN-001B-D7a 0.6 parameter uncertainties E MeV 60 10 60 NuPECC Meeting,Vienna, March 13, 2009

  25. Importanceofuncertaintyinformation cross section covariances Safetymargins – commissioning Reducethenumberof experimental tests  significanteconomicimpact NuPECC Meeting,Vienna, March 13, 2009

  26. ImplementationofBayesianstatistics Bayes Theorem (1763): p(x|s M) = p(s |xM) p(x|M) / p(s |M) posterior = likelihood x prior/ evidence x ... model parameters ... dataM ... otherinformation NuPECC Meeting,Vienna, March 13, 2009

  27. Bayesian update procedure prior x0 M0 Exp-01 x1 M1 Exp-02 x2 M2 Exp-03 x3 M3 Exp-m xm Mm experiment posterior NuPECC Meeting,Vienna, March 13, 2009

  28. Problem of update procedure prior systematic error statistical error Bayes theorem Bayesian update NuPECC Meeting,Vienna, March 13, 2009

  29. Origin ofthedifference The ‚experiments‘ covariance matrix V contains all experiments and all correlations Standard Bayesian update procedure – no correlations between experiments Systematic errors are treated like a statistical uncertainty i.e. NuPECC Meeting,Vienna, March 13, 2009

  30. Evaluation Tool GENEUS still manual semi-automatic for single isotope and restricted reaction channels not available ENDF-file tables graphics PRIOR TALYS SC2COV BAYES SCALE one-step procedure EXFOR Janis-Tables EXPCOV NuPECC Meeting,Vienna, March 13, 2009

  31. Perspectives • CurrentDemands: • Inclusionofuncertaintyinformation covariancematrices • Extension ofenergyrangeto ~150MeV • Challenges: • Evaluation processandcovariancematrices – scarcityof experimental datafor E > 20 MeV quest ofuncertaintyofnuclearmodels • Improvementofmodels: nuclearreactions, fission, … NuPECC Meeting,Vienna, March 13, 2009

  32. Topics in nuclearreactions • Future research will focus on challenges in reactiontheory: • Reactionsinvolvingchargedcompositenucleiembrittlement due to gas production in structurematerials p-processreactions in nuclearastrophysics, (a,g), (p,g) • Reactionsinvolvingweaklyboundnucleibreak-upcontributions in deuteroninvolvingreactionsreactionprocesseswithexoticweaklyboundnuclei • (Microscopic) modellingofnuclearfissionmicroscopicunderstandingoffissionprocessmodellingoffissioncrosssectionsexperimentally not accessible isotopes (MA) NuPECC Meeting,Vienna, March 13, 2009

  33. Summaryandoutlook • Summary: • Neutron-inducedcrosssectionmeasured • Well definedevaluationprocedurebased on modellingdeveloped • General evaluationtool GENEUS isunderconstruction Outlook: Focus iscurrentlychangingtotopics on reactiontheory - compositeparticlescatteringtheory - reactionsinvolvingweaklyboundnuclei NuPECC Meeting,Vienna, March 13, 2009

  34. Working Group J. Gundacker (Master) J. Haidvogl (PhD) D. Neudecker (PhD) Th. Srdinko (Master) V. Wildpaner Former students K. Nikolics M.T. Pigni (PhD) I. Raskinyte (PostDoc) EU Research Projects: EURATOM P&T: n_TOF,IP_EUROTRANS EURATOM Fusion: EFDA-Projrects, F4E-Grants EU I3-Project: EURONS Strong collaborationwiththe nucleardatacenters NEA, IAEA NuPECC Meeting,Vienna, March 13, 2009

  35. THANK YOU FOR YOUR ATTENTION NuPECC Meeting,Vienna, March 13, 2009

  36. a-nucleusopticalpotentials (semi)microscopic approach for low energies (relevant to astrophysics) Optical Potential: direct term coupling term Direct part: evaluated within RGM in order to account correctly for the antisymmetrisation NuPECC Meeting,Vienna, March 13, 2009

  37. Imaginarya-nucleusopticalpotentials Imaginary Part: Intermediate states in RPA Green functionat intermediate state Itcanbeconsideredas a nuclearstructureapproachtoa-nucleus optical potential, whichshouldworksatisfactoryatlowenergies calculationsfora-16O anda-40Ca anda-208Pb are in progress NuPECC Meeting,Vienna, March 13, 2009

  38. Reactionsofweaklyboundnuclei deuteron breaks up easily (EB=2,2 MeV) breakup leads to additional flux loss Incoming channel outgoing channel Elastic d-A channel Incoming d-A channel Breakup of the deuteron nonelastic due to n-collision nonelastic due to p-collision Neglecting breakup leads to non-standard parameters in fitted potentials Keaton, Armstrong (1973) Ansatz of a complete wave function of the d-A system deuteron wave function p-n scattering wave function (continuum) NuPECC Meeting,Vienna, March 13, 2009

  39. Breakupcontributionford-6Li NuPECC Meeting,Vienna, March 13, 2009

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