2. 05/31/2007Teppei Katori, Indiana University, NuInt '07 1 Charged Current Interaction measurements in MiniBooNE hep-ex/XXX Teppei Katori for the MiniBooNE collaboration Indiana University NuInt 07, Fermilab, May., 31, 07

3. 05/31/2007Teppei Katori, Indiana University, NuInt '07 2 outline 1. CCQE events in MiniBooNE 2. Prediction for CCQE events 3. CCQE data-MC comparison 4. Fit results 5. Anti-neutrino CCQE events 6. Conclusion Charged Current Interaction measurements in MiniBooNE hep-ex/XXX

4. 05/31/2007Teppei Katori, Indiana University, NuInt '07 3 1. CCQE events in MiniBooNE

5. 05/31/2007Teppei Katori, Indiana University, NuInt '07 4 1. CCQE events in MiniBooNE  charged current quasi-elastic (  CCQE) interaction is the most abundant (~40%) and the fundamental interaction in MiniBooNE detector n 12 C MiniBooNE detector (spherical Cherenkov detector)

6. 05/31/2007Teppei Katori, Indiana University, NuInt '07 5 1. CCQE events in MiniBooNE  charged current quasi-elastic (  CCQE) interaction is the most abundant (~40%) and the fundamental interaction in MiniBooNE detector p  n -beam (Scintillation) Cherenkov 1 12 C MiniBooNE detector (spherical Cherenkov detector) muon like Cherenkov light and subsequent decayed electron (Michel electron) like Cherenkov light are the signal of CCQE event Cherenkov 2 e

7. 05/31/2007Teppei Katori, Indiana University, NuInt '07 6  CCQE interactions ( +n   +p) has characteristic two “subevent” structure from muon decay   + n   + p   +  e + e 35.0% cut efficiency 197,308 events with 5.58E20POT 1. CCQE events in MiniBooNE muon >200 hits Michel electron <200 hits

8. 05/31/2007Teppei Katori, Indiana University, NuInt '07 7 total 2 subevents54.2% muon in beam window (4400ns < Time < 6400ns)52.9% muon veto hits < 6 and Michel electron veto hits < 646.4% muon tank hits > 200 and Michel electron tank hits < 20041.6% fiducial reconstruction for muon41.3% muon and electron distance < 100cm35.0% Cut and efficiency summary 1. CCQE events in MiniBooNE

9. 05/31/2007Teppei Katori, Indiana University, NuInt '07 8 All kinematics are specified from 2 observables, muon energy E  and muon scattering angle  Energy of the neutrino E and 4-momentum transfer Q 2 can be reconstructed by these 2 observables 1. CCQE events in MiniBooNE  12 C -beam cos  EE

10. 05/31/2007Teppei Katori, Indiana University, NuInt '07 9 2. Prediction for CCQE events

11. 05/31/2007Teppei Katori, Indiana University, NuInt '07 10 Predicted event rates (NUANCE Monte Carlo) Casper, Nucl.Phys.Proc.Suppl. 112 (2002) 161 2. Prediction for CCQE events

12. 05/31/2007Teppei Katori, Indiana University, NuInt '07 11 Relativistic Fermi Gas (RFG) Model Carbon is described by the collection of incoherent Fermi gas particles. All details come from hadronic tensor. 2. Prediction for CCQE events Smith and Moniz, Nucl.,Phys.,B43(1972)605

13. 05/31/2007Teppei Katori, Indiana University, NuInt '07 12 Relativistic Fermi Gas (RFG) Model Carbon is described by the collection of incoherent Fermi gas particles. All details come from hadronic tensor. 2. Prediction for CCQE events Smith and Moniz, Nucl.,Phys.,B43(1972)605 3 parameters are especially important to control nuclear effect of Carbon; M A = 1.03GeV : axial mass P F = 220MeV : Fermi momentum E B = 34MeV : binding energy

14. 05/31/2007Teppei Katori, Indiana University, NuInt '07 13 3. CCQE data-MC comparison

15. 05/31/2007Teppei Katori, Indiana University, NuInt '07 14 3. CCQE data-MC comparison Since data-MC disagreements align on the Q 2 lines, not E lines, the source of data-MC disagreement is not the neutrino beam prediction, but the neutrino cross section prediction. data-MC ratio from RFG model CCQE kinematics phase space The data-MC agreement is not great

16. 05/31/2007Teppei Katori, Indiana University, NuInt '07 15 3. CCQE data-MC comparison The data-MC disagreement is characterized by 2 features; (1) data deficit at low Q 2 region (2) data excess at high Q 2 region data-MC ratio from RFG model CCQE kinematics phase space The data-MC agreement is not great

17. 05/31/2007Teppei Katori, Indiana University, NuInt '07 16 3. CCQE data-MC comparison Nuclear model parameters are tuned from electron scattering data, thus the best explanations of observed data-MC disagreements are something one cannot measure from the electron scattering data (1) data deficit at low Q 2 region  Pauli blocking (2) data excess at high Q 2 region  Axial mass M A We tune the nuclear parameters in RFG model using Q 2 distribution; M A = tuned P F = fixed E B = fixed

18. 05/31/2007Teppei Katori, Indiana University, NuInt '07 17 3. CCQE data-MC comparison Pauli blocking parameter "kappa" :  To enhance the Pauli blocking at low Q 2, we introduced a new parameter , which is the scale factor of lower bound of nucleon sea and controls the size of nucleon phase space We tune the nuclear parameters in RFG model using Q 2 distribution; M A = tuned P F = fixed E B = fixed  = tuned Ehi(fixed) Elo(tuned) px py pz px py pz This modification gives significant effect only at low Q 2 region

19. 05/31/2007Teppei Katori, Indiana University, NuInt '07 18 3. CCQE data-MC comparison Q 2 distribution with M A variation Q 2 distribution with  variation M A and  are simultaneously fit to the data 2% change of  is sufficient to take account the data deficit at low Q 2 region

20. 05/31/2007Teppei Katori, Indiana University, NuInt '07 19 4. Fit results

21. 05/31/2007Teppei Katori, Indiana University, NuInt '07 20 4. Fit results Least  2 fit for Q 2 distribution  2 = (data - MC) T (M total ) -1 (data - MC)  2 minimum is found by global scan of shape only fit with 0.0<Q 2 (GeV 2 )<1.0 The total output error matrix keep the correlation of Q 2 bins M total = M(  + production) + M(  - production) + M(K + production) + M(K 0 production) + M(beam model) + M(cross section model) + M(detector model) + M(data statistics)  + production (8 parameters)  - production (8 parameters) K + production (7 parameters) K 0 production (9 parameters) beam model (8 parameters) cross section (20 parameters) detector model (39 parameters) dependent independent Input error matrices keep the correlation of systematics

22. 05/31/2007Teppei Katori, Indiana University, NuInt '07 21 dots : data with error bar dashed line : before fit solid line : after fit dotted line : background dash-dotted :non-CCQElike bkgd 4. Fit results M A -  fit result M A = 1.23 ± 0.20(stat+sys)  = 1.019 ± 0.011(stat+sys) circle: before fit star: after fit with 1-sigma contour triangle: bkgd shape uncertainty

23. 05/31/2007Teppei Katori, Indiana University, NuInt '07 22 4. Fit results  M A (GeV)  data statistics0.030.003 neutrino flux0.040.003 neutrino cross section0.060.004 detector model0.100.003 CC  + background shape 0.020.007 total error0.200.011 Errors The detector model uncertainty dominates the error in M A The error on  is dominated by Q2 shape uncertainty of background events

24. 05/31/2007Teppei Katori, Indiana University, NuInt '07 23 4. Fit results Although fit is done in Q 2 distribution, entire CCQE kinematics is improved before  2 /dof = 79.5/53, P(  2 ) = 1%  after  2 /dof = 45.1/53, P(  2 ) = 77% data-MC ratio after the fit M A -  fit result M A = 1.23 ± 0.20(stat+sys)  = 1.019 ± 0.011(stat+sys)

25. 05/31/2007Teppei Katori, Indiana University, NuInt '07 24 data-MC ratio before the fit 4. Fit results M A -  fit result M A = 1.23 ± 0.20(stat+sys)  = 1.019 ± 0.011(stat+sys) data-MC ratio after the fit Although fit is done in Q 2 distribution, entire CCQE kinematics is improved before  2 /dof = 79.5/53, P(  2 ) = 1%  after  2 /dof = 45.1/53, P(  2 ) = 77%

26. 05/31/2007Teppei Katori, Indiana University, NuInt '07 25 4. Fit results E distribution cos   distribution Other kinematics distribution also show very good data-MC agreement (This is critical for MiniBooNE neutrino oscillation search experiment) MiniBooNE collaboration, arXiv:0704.1500 [hep-ex] (2007)

27. 05/31/2007Teppei Katori, Indiana University, NuInt '07 26 fit with fixing  for 0.25<Q 2 (GeV 2 )<1.0 good agreement above 0.25GeV 2 but gross disagreement at low Q 2 region This fit cannot improve entire CCQE phase space 4. Fit results M A only fit result M A = 1.25 ± 0.12(stat+sys) Q 2 distribution

28. 05/31/2007Teppei Katori, Indiana University, NuInt '07 27 4. Fit results M A only fit result M A = 1.25 ± 0.12(stat+sys) data-MC ratio after the fit fit with fixing  for 0.25<Q 2 (GeV 2 )<1.0 good agreement above 0.25GeV 2 but gross disagreement at low Q 2 region This fit cannot improve entire CCQE phase space

29. 05/31/2007Teppei Katori, Indiana University, NuInt '07 28 5. Anti-neutrino CCQE events

30. 05/31/2007Teppei Katori, Indiana University, NuInt '07 29 5. Anti-neutrino CCQE events Anti-neutrino Q 2 distribution MiniBooNE anti-neutrino CCQE 8772 events (1651 total for pre-MiniBooNE data) We use same cut with neutrino mode The values of M A and  extracted from neutrino mode are employed to anti- neutrino MC, and they describe data Q 2 distribution well. Anti-neutrino Q 2 distribution data with stat error Preliminary

31. 05/31/2007Teppei Katori, Indiana University, NuInt '07 30 5. Anti-neutrino CCQE events Anti-neutrino Q 2 distribution MiniBooNE anti-neutrino CCQE 8772 events (1651 total for pre-MiniBooNE data) We use same cut with neutrino mode The values of M A and  extracted from neutrino mode are employed to anti- neutrino MC, and they describe data Q 2 distribution well. Anti-neutrino Q 2 distribution data-MC ratio Preliminary

32. 05/31/2007Teppei Katori, Indiana University, NuInt '07 31 5. Anti-neutrino CCQE events Anti-neutrino CCQE kinematics MiniBooNE anti-neutrino CCQE 8772 events (1651 total for pre-MiniBooNE data) We use same cut with neutrino mode The values of M A and  extracted from neutrino mode are employed to anti- neutrino MC, and they describe data Q 2 distribution well. Anti-neutrino CCQE kinematics variables are described by the MC well, too. MA = 1.23GeV, k=1.019 data with stat error  kinematics Preliminary

33. 05/31/2007Teppei Katori, Indiana University, NuInt '07 32 6. Conclusion MiniBooNE has large CCQE data set around 1GeV region MiniBooNE successfully employee RFG model with appropriate parameter choices for M A and  This new model can describe entire CCQE phase space well The best fit parameters for MiniBooNE CCQE data are; M A = 1.23 ± 0.20(stat+sys)  = 1.019 ± 0.011(stat+sys) Our new model also works well in anti-neutrino data MiniBooNE is currently taking the data with anti-muon neutrino beam

34. 05/31/2007Teppei Katori, Indiana University, NuInt '07 33 MiniBooNE collaboration University of Alabama Los Alamos National Laboratory Bucknell University Louisiana State University University of Cincinnati University of Michigan University of Colorado Princeton University Columbia University Saint Mary’s University of Minnesota Embry Riddle University Virginia Polytechnic Institute Fermi National Accelerator Laboratory Western Illinois University Indiana University Yale University Thank you for your attention!

35. 05/31/2007Teppei Katori, Indiana University, NuInt '07 34 10. Back up

36. 05/31/2007Teppei Katori, Indiana University, NuInt '07 35 Fit quality (  2 probability) is good even Q 2 min =0.0GeV 2 MA is stable in wide range of Q 2 min Since  is only important for low Q 2 region, it has no power for fit for high Q 2 4. Fit results Fit result with varying Q 2 min, Q 2 min < Q 2 < 1.0GeV 2 Fit is repeated with changing the Q 2 min 0.0 0.1 0.2 0.3 0.4 0.5(GeV 2 ) 0.6 0.4 0.2 0.0  2 probability 1.050 1.025 1.000  1.50 1.25 1.00 M A (GeV 2 ) 0.0 0.1 0.2 0.3 0.4 0.5(GeV 2 )

37. 05/31/2007Teppei Katori, Indiana University, NuInt '07 36 Fit quality (  2 probability) is low for Q 2 min < 0.2GeV 2 MA is stable in wide range of Q 2 min for Q 2 min > 0.2GeV 2 4. Fit results M A only fit with varying Q 2 min, Q 2 min < Q 2 < 1.0GeV 2 Fit is repeated with changing the Q 2 min 0.6 0.4 0.2 0.0  2 probability 1.50 1.25 1.00 M A (GeV 2 ) 0.0 0.1 0.2 0.3 0.4 0.5(GeV 2 )

38. 05/31/2007Teppei Katori, Indiana University, NuInt '07 37 Modeling Production of Secondary Pions - 5%  Beryllium target - 8.9 GeV proton beam momentum HARP collaboration, hep-ex/0702024 Data are fit to a Sanford-Wang parameterization. 3. Neutrino beam HARP experiment (CERN)

39. 05/31/2007Teppei Katori, Indiana University, NuInt '07 38 Modeling Production of Secondary Kaons K + Data from 10 - 24 GeV. Uses a Feynman Scaling Parameterization. K 0 data are also parameterized. In situ measurement of K + from LMC agrees within errors with parameterization 3. Neutrino beam

40. 05/31/2007Teppei Katori, Indiana University, NuInt '07 39 N   N  25% Events producing pions CC  + Easy to tag due to 3 subevents. Not a substantial background to the oscillation analysis. NC  0 The  0 decays to 2 photons, which can look “electron-like” mimicking the signal... <1% of  0 contribute to background. N  00 N 8% (also decays to a single photon with 0.56% probability) 5. Cross section model

41. 05/31/2007Teppei Katori, Indiana University, NuInt '07 40 K       “Intrinsic” e +  e sources:    e +    e (52%)  K +    e + e (29%)  K 0   e e (14%)  Other ( 5%) e /  = 0.5% Antineutrino content: 6% 6. Blind analysis Since MiniBooNE is blind analysis experiment, we need to constraint intrinsic e background without measuring directly (1)  decay e background (2) K decay e background   e   e K   e e

42. 05/31/2007Teppei Katori, Indiana University, NuInt '07 41 6. Blind analysis (1) measure  flux from  CCQE event to constraint e background from  decay  CCQE is one of the open boxes. Kinematics allows connection to  flux, hence intrinsic e background from  decay is constraint. hit time energy veto hits CCQE NC high energy   e   e    E  (GeV) E  (GeV) E = 0.43 E  E -E  space

43. 05/31/2007Teppei Katori, Indiana University, NuInt '07 42 6. Blind analysis (2) measure high energy  events to constraint e background from K decay At high energies, above “signal range”  and “ e -like” events are largely due to kaon decay energy veto hits CCQE NC high energy    K   e e K    signal range events Dominated by Kaon decay example of open boxes;  -   CCQE - high energy event - CC  + - NC elastics - NC   - NC electron scattering - Michel electron etc.... hit time

44. 05/31/2007Teppei Katori, Indiana University, NuInt '07 43 Handling uncertainties in the analyses: For a given source of uncertainty, Errors on a wide range of parameters in the underlying model For a given source of uncertainty, Errors in bins of E QE and information on the correlations between bins What we begin with...... what we need 7. Error analysis Input error matrix keep the all correlation of systematics Output error matrix keep the all correlation of E QE bins "multisim" nonlinear error propagation

45. 05/31/2007Teppei Katori, Indiana University, NuInt '07 44 Multi-simulation (Multisim) method many fake experiments with different parameter set give the variation of correlated systematic errors for each independent error matrix total error matrix is the sum of all independent error matrix 7. Multisim  + production (8 parameters)  - production (8 parameters) K + production (7 parameters) K 0 production (9 parameters) beam model (8 parameters) cross section (20 parameters) detector model (39 parameters) dependent independent Input error matrices B.P.Roe, Nucl.,Instrum.,Meth,A570(2007)157

46. 05/31/2007Teppei Katori, Indiana University, NuInt '07 45 M A QE 6% E lo sf 2% QE  norm 10% ex) cross section uncertainties 7. Multisim correlated uncorrelated cross section error for E QE repeat this exercise many times to create smooth error matrix for E QE 1 st cross section model 2 nd cross section model 3 rd cross section model... n 1 n 2 n 3 n 4 n 5 n 6 n 7 n 8 E QE (GeV) QE  norm E lo MAMA cross section parameter space Input cross section error matrix

47. 05/31/2007Teppei Katori, Indiana University, NuInt '07 46 M A QE 6% E lo sf 2% QE  norm 10% ex) cross section uncertainties 7. Multisim correlated uncorrelated Input cross section error matrix cross section error for E QE repeat this exercise many times to create smooth error matrix for E QE 1 st cross section model 2 nd cross section model 3 rd cross section model... n 1 n 2 n 3 n 4 n 5 n 6 n 7 n 8 E QE (GeV) QE  norm E lo MAMA cross section parameter space

48. 05/31/2007Teppei Katori, Indiana University, NuInt '07 47 7. Multisim Output cross section error matrix for E QE cross section error for E QE Oscillation analysis use output error matrix for  2 fit;  2 = (data - MC) T (M output ) -1 (data - MC) 1 st cross section model 2 nd cross section model 3 rd cross section model... n 1 n 2 n 3 n 4 n 5 n 6 n 7 n 8 E QE (GeV)

49. 05/31/2007Teppei Katori, Indiana University, NuInt '07 48 M A QE 6% E lo sf 2% QE  norm 10% QE  shape function of E  e /  QE  function of E NC  0 rate function of  0 mom M A coh, coh  ±25%   N  rate function of  mom + 7% BF E B, p F 9 MeV, 30 MeV  s 10% M A 1  25% M A N  40% DIS  25% etc... determined from MiniBooNE  QE data determined from MiniBooNE  NC  0 data ex) cross section uncertainties determined from other experiments 7. Multisim