03/07/2007Teppei Katori, Indiana University, PMN07 1 Neutrino Interaction measurements in MiniBooNE hep-ex/ Teppei Katori and D. Chris Cox for the MiniBooNE collaboration Indiana University PMN07, Blaubeuren, July, 03, 07

03/07/2007Teppei Katori, Indiana University, PMN07 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. Neutral Current Elastic events 7. SciBooNE experiment 8. Conclusion Neutrino Interaction measurements in MiniBooNE hep-ex/

03/07/2007Teppei Katori, Indiana University, PMN CCQE events in MiniBooNE

03/07/2007Teppei Katori, Indiana University, PMN 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)

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN07 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

03/07/2007Teppei Katori, Indiana University, PMN Prediction for CCQE events

03/07/2007Teppei Katori, Indiana University, PMN07 8 Predicted event rates (NUANCE Monte Carlo) Casper, Nucl.Phys.Proc.Suppl. 112 (2002) Prediction for CCQE events

03/07/2007Teppei Katori, Indiana University, PMN07 9 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

03/07/2007Teppei Katori, Indiana University, PMN07 10 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

03/07/2007Teppei Katori, Indiana University, PMN CCQE data-MC comparison

03/07/2007Teppei Katori, Indiana University, PMN 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 parameter kappa:  (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  = tuned

03/07/2007Teppei Katori, Indiana University, PMN Fit results

03/07/2007Teppei Katori, Indiana University, PMN07 14 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)  = ± 0.011(stat+sys) circle: before fit star: after fit with 1-sigma contour triangle: bkgd shape uncertainty

03/07/2007Teppei Katori, Indiana University, PMN07 15 data-MC ratio before the fit 4. Fit results M A -  fit result M A = 1.23 ± 0.20(stat+sys)  = ± 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%

03/07/2007Teppei Katori, Indiana University, PMN 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, PRL98(2007)231801

03/07/2007Teppei Katori, Indiana University, PMN Anti-neutrino CCQE events

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN Neutral Current Elastic events

03/07/2007Teppei Katori, Indiana University, PMN Neutral Current Elastic events  s (strange quark spin component in the nucleon) it can be measured by neutrino-nucleon Neutral Current Elastic (NCE) scattering and  s gives the connection between elastic scattering physics and DIS physics Axial current (neutrino physics) Polarized strange quark PDF (spin physics) BNL E734 (~ -0.2) SMC (~ -0.1) HERMES (~ 0.0)  s is interesting open question!

03/07/2007Teppei Katori, Indiana University, PMN Neutral Current Elastic events protons electrons Prompt hits fraction  -decay e time distribution prompt Cherenkov delayed scintillation NCE events in MiniBooNE - low tank hits (no Cherenkov) - time structure (scintillation is slower) -> 84% NCE purity

03/07/2007Teppei Katori, Indiana University, PMN Neutral Current Elastic events MiniBooNE predicts 5:4 ratio NC p : NC n cross sections Currently we cannot measure the  s, because non-zero  s increases/decreases  (NCp)/  (NCn) however, we can measure the ensemble of  (NCp) and  (NCn), which can be used for M A extraction (ongoing project) ensemble NCE cross section shows good agreement with BNL E734

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE experiment

03/07/2007Teppei Katori, Indiana University, PMN07 25 DIS QE 11 7. SciBooNE SciBar detector (used by K2K experiment) is shipped from Japan. Goal of SciBooNE is to measure the cross section around 0.8GeV, where the most important energy scale for T2K experiment. MINOS, NuMI K2K, NOvA MiniBooNE, T2K, SciBooNE 250km Decay region 50 m MiniBooNE Detector SciBar MiniBooNE beamline 100 m 440 m

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE Extruded scintillator (15t) Multi-anode PMT (64 ch.) Wave-length shifting fiber 1.7m 3m SciBar detector Extruded scintillators with WLS fiber readout by multi-anode PMT

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE EM calorimeter 1.7m 3m MRD (Muon Range Detector) iron plates with X-Y scintillator panels Measure the muon momentum up to 1.2GeV EC (electron catcher) lead with scintillation fiber "Spaghetti" calorimeter to see electrons e 

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE SciBar installation

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE Stay tuned... MRD installation

03/07/2007Teppei Katori, Indiana University, PMN SciBooNE First neutrino candidate event in SciBooNE (May 30, 07) Mar K2K ends Nov SciBooNE is proposed Dec Approved Mar First collaboration meeting Jul SciBar arrived from Japan Sep Groundbreaking Apr SciBar assembly completed Apr Detectors move to the hall May Cabling (~15,000 channel) Jun SciBooNE started to take anti-neutrino beam data In SciBooNE, students can work all components of the experiment, including hardware, software, analysis, and the publication we need this kind of experiment for students

03/07/2007Teppei Katori, Indiana University, PMN Conclusion MiniBooNE successfully employee RFG model with appropriate parameter choices for M A and  for CCQE data The best fit parameters for MiniBooNE CCQE data are; M A = 1.23 ± 0.20(stat+sys)  = ± 0.011(stat+sys) The paper is available on online hep-ex/ , submitted to PRL Our new model also works well in anti-neutrino data Neutral Current Elastic events cross section is measured SciBooNE experiment started to take data since June 2007 MiniBooNE is currently taking the data with anti-muon neutrino beam

03/07/2007Teppei Katori, Indiana University, PMN07 32 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!

03/07/2007Teppei Katori, Indiana University, PMN Back up

03/07/2007Teppei Katori, Indiana University, PMN07 34 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 < % fiducial reconstruction for muon41.3% muon and electron distance < 100cm35.0% Cut and efficiency summary 1. CCQE events in MiniBooNE

03/07/2007Teppei Katori, Indiana University, PMN07 35 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN Fit results  M A (GeV)  data statistics neutrino flux neutrino cross section detector model CC  + background shape total error Errors The detector model uncertainty dominates the error in M A The error on  is dominated by Q2 shape uncertainty of background events

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN 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)  = ± 0.011(stat+sys)

03/07/2007Teppei Katori, Indiana University, PMN07 43 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

03/07/2007Teppei Katori, Indiana University, PMN 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

03/07/2007Teppei Katori, Indiana University, PMN07 45 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 (GeV 2 )  2 probability  M A (GeV 2 ) (GeV 2 )

03/07/2007Teppei Katori, Indiana University, PMN07 46 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  2 probability M A (GeV 2 ) (GeV 2 )

03/07/2007Teppei Katori, Indiana University, PMN 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