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The first result of the MiniBooNE neutrino oscillation experiment

The first result of the MiniBooNE neutrino oscillation experiment. Teppei Katori for the MiniBooNE collaboration Indiana University McGill University, Montreal, May., 09, 07. The first result of the MiniBooNE neutrino oscillation experiment.

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The first result of the MiniBooNE neutrino oscillation experiment

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  1. The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE collaboration Indiana University McGill University, Montreal, May., 09, 07 Teppei Katori, McGill University HEP seminar

  2. The first result of the MiniBooNE neutrino oscillation experiment outline1. Neutrino oscillation2. LSND experiment3. Neutrino beam4. Events in the detector5. Cross section model6. Oscillation analysis7. Systematic error analysis8. The MiniBooNE initial results9. Low energy excess events10. Future plans Teppei Katori, McGill University HEP seminar

  3. 1. Neutrino Oscillation Teppei Katori, McGill University HEP seminar

  4. 1. Neutrino oscillation The neutrino weak eigenstate is described by neutrino Hamiltonian eigenstates, n1, n2, and n3 and their mixing matrix elements. The time evolution of neutrino weak eigenstate is written by Hamiltonian mixing matrix elements and eigenvalues of n1, n2, and n3. Then the transition probability from weak eigenstate nmto ne is So far, model independent Teppei Katori, McGill University HEP seminar

  5. 1. Neutrino oscillation From here, model dependent formalism. In the vacuum, 2 neutrino state effective Hamiltonian has a form, Therefore, 2 massive neutrino oscillation model is Or, conventional form Teppei Katori, McGill University HEP seminar

  6. 2. LSND experiment Teppei Katori, McGill University HEP seminar

  7. L/E~30m/30MeV~1 LSND signal 2. LSND experiment LSND experiment at Los Alamos observed excess of anti-electron neutrino events in the anti-muon neutrino beam. 87.9 ± 22.4 ± 6.0 (3.8.s) LSND Collaboration, PRD 64, 112007 Teppei Katori, McGill University HEP seminar

  8. 2. LSND experiment Dm132 = Dm122 + Dm232 increasing (mass) 2 3 types of neutrino oscillations are found: LSND neutrino oscillation: Dm2~1eV2 Atmospheric neutrino oscillation: Dm2~10-3eV2 Solar neutrino oscillation : Dm2~10-5eV2 But we cannot have so many Dm2! We need to test LSND signal MiniBooNE experiment is designed to have same L/E~500m/500MeV~1 to test LSND Dm2~1eV2 Teppei Katori, McGill University HEP seminar

  9. 2. MiniBooNE experiment nm ne??? FNAL Booster target and horn decay region absorber detector K+ p+ Booster dirt primary beam secondary beam tertiary beam (protons) (mesons) (neutrinos) Keep L/E same with LSND, while changing systematics, energy & event signature; P(nm-ne)= sin22q sin2(1.27Dm2L/E) MiniBooNE is looking for the single isolated electron like events, which is the signature of ne events MiniBooNE has; - higher energy (~500 MeV) than LSND (~30 MeV) - longer baseline (~500 m) than LSND (~30 m) Teppei Katori, McGill University HEP seminar

  10. 3. Neutrino beam Teppei Katori, McGill University HEP seminar

  11. 3. Neutrino beam Booster Target Hall FNAL Booster target and horn decay region absorber detector dirt nm ne??? K+ p+ Booster primary beam secondary beam tertiary beam (protons) (mesons) (neutrinos) MiniBooNE extracts beam from the 8 GeV Booster Teppei Katori, McGill University HEP seminar

  12. 3. Neutrino beam Magnetic focusing horn nm ne??? target and horn FNAL Booster decay region absorber detector K+ p+ Booster dirt primary beam secondary beam tertiary beam (protons) (mesons) (neutrinos) 8GeV protons are delivered to a 1.7 l Be target within a magnetic horn (2.5 kV, 174 kA) that (increases the flux by  6) p- p+ p+ p- Teppei Katori, McGill University HEP seminar

  13. 3. Neutrino beam HARP experiment (CERN) Modeling Production of Secondary Pions - 5% l Beryllium target - 8.9 GeV proton beam momentum Data are fit to a Sanford-Wang parameterization. HARP collaboration, hep-ex/0702024 Teppei Katori, McGill University HEP seminar

  14. 3. Neutrino beam p m nm Km nm m e nm ne Kp e ne Neutrino Flux from GEANT4 Simulation MiniBooNE is the ne appearance oscillation experiment “Intrinsic”ne + nesources: • m+e+nm ne (52%) • K+ p0 e+ne (29%) • K0 p e ne (14%) • Other ( 5%) ne/nm = 0.5% Antineutrino content: 6% Teppei Katori, McGill University HEP seminar

  15. 4. Events in the Detector Teppei Katori, McGill University HEP seminar

  16. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo Teppei Katori, McGill University HEP seminar

  17. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo 541 meters Booster Teppei Katori, McGill University HEP seminar

  18. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo Teppei Katori, McGill University HEP seminar

  19. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo Teppei Katori, McGill University HEP seminar

  20. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo Extinction rate of MiniBooNE oil Teppei Katori, McGill University HEP seminar

  21. 4. Events in the Detector • The MiniBooNE Detector • - 541 meters downstream of target • - 3 meter overburden • - 12 meter diameter sphere • (10 meter “fiducial” volume) • - Filled with 800 t of pure mineral oil (CH2) • (Fiducial volume: 450 t) • - 1280 inner phototubes, • - 240 veto phototubes • Simulated with a GEANT3 Monte Carlo Teppei Katori, McGill University HEP seminar

  22. 4. Events in the Detector nmcharged current quasi-elastic (nmCCQE) interaction is the most abundant (~40%) and the fundamental interaction in MiniBooNE detector 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 1 e m n-beam 12C Cherenkov 2 n p (Scintillation) Teppei Katori, McGill University HEP seminar

  23. 4. Events in the Detector Number of tank hits for CCQE event m e 19.2 ms beam trigger window with the 1.6ms spill Multiple hits within a ~100 ns window form “subevents” nm CCQE interactions (n+n m+p) with characteristic two “subevent” structure from stopped mnmnee Teppei Katori, McGill University HEP seminar

  24. 4. Events in the Detector Times of hit-clusters (subevents) Beam spill (1.6ms) is clearly evident simple cuts eliminate cosmic backgrounds Neutrino Candidate Cuts <6 veto PMT hits Gets rid of muons >200 tank PMT hits Gets rid of Michels Only neutrinos are left! Beam and Cosmic BG Teppei Katori, McGill University HEP seminar

  25. 4. Events in the Detector Times of hit-clusters (subevents) Beam spill (1.6ms) is clearly evident simple cuts eliminate cosmic backgrounds Neutrino Candidate Cuts <6 veto PMT hits Gets rid of muons >200 tank PMT hits Gets rid of Michels Only neutrinos are left! Beam and Michels Teppei Katori, McGill University HEP seminar

  26. 4. Events in the Detector Times of hit-clusters (subevents) Beam spill (1.6ms) is clearly evident simple cuts eliminate cosmic backgrounds Neutrino Candidate Cuts <6 veto PMT hits Gets rid of muons >200 tank PMT hits Gets rid of Michels Only neutrinos are left! Beam Only Teppei Katori, McGill University HEP seminar

  27. 4. Events in the Detector • Muons • Sharp, clear rings • Long, straight tracks • Electrons • Scattered rings • Multiple scattering • Radiative processes • Neutral Pions • Double rings • Decays to two photons Teppei Katori, McGill University HEP seminar

  28. 4. Events in the Detector • Muons • Sharp, clear rings • Long, straight tracks • Electrons • Scattered rings • Multiple scattering • Radiative processes • Neutral Pions • Double rings • Decays to two photons Teppei Katori, McGill University HEP seminar

  29. 4. Events in the Detector • Muons • Sharp, clear rings • Long, straight tracks • Electrons • Scattered rings • Multiple scattering • Radiative processes • Neutral Pions • Double rings • Decays to two photons Teppei Katori, McGill University HEP seminar

  30. 4. Events in the Detector • Muons • Sharp, clear rings • Long, straight tracks • Electrons • Scattered rings • Multiple scattering • Radiative processes • Neutral Pions • Double rings??? • Decays to two photons??? • Looks like the electron (the biggest misID) Teppei Katori, McGill University HEP seminar

  31. 5. Cross section model Teppei Katori, McGill University HEP seminar

  32. 5. Cross section model Event neutrino energy (GeV) Predicted event rates before cuts (NUANCE Monte Carlo) Casper, Nucl.Phys.Proc.Suppl. 112 (2002) 161 Teppei Katori, McGill University HEP seminar

  33. 5. Cross section model Evisible m 12C n-beam cosq CCQE (Charged Current Quasi-Elastic) - 39% of total - Events are “clean” (few particles) - Energy of the neutrino (EnQE) can be reconstructed from; - Scattering angle cosq - Visible energy Evisible Teppei Katori, McGill University HEP seminar

  34. 5. Cross section model Q2 distribution before and after fitting The data-MC agreement in Q2 (4-momentum transfer) distribution is not great We tuned nuclear parameters in Relativistic Fermi Gas model Smith and Moniz, Nucl.,Phys.,B43(1972)605 Q2 fits to MBnmCCQE data using the nuclear parameters: MAeff - effective axial mass EloSF - Pauli Blocking parameter Relativistic Fermi Gas Model with tuned parameters describes nmCCQE data well (paper in preparation) Teppei Katori, McGill University HEP seminar

  35. 5. Cross section model data-MC ratio after the fit En= 0.3GeV 0.6GeV 1.0GeV 2.0GeV Q2=0.2GeV2 Q2=0.6GeV2 Q2=1.0GeV2 Q2=2.0GeV2 data-MC ratio is flat through entire kinematic space made by CCQE interaction Teppei Katori, McGill University HEP seminar

  36. 6. Oscillation analysis Teppei Katori, McGill University HEP seminar

  37. 6. Blind analysis The MiniBooNE signal is small but relatively easy to isolate The data is described in n-dimensional space; hit time veto hits energy Teppei Katori, McGill University HEP seminar

  38. 6. Blind analysis CCQE ne candidate (closed box) The MiniBooNE signal is small but relatively easy to isolate The data is described in n-dimensional space; hit time NC veto hits high energy energy The data is classified into "box". For boxes to be "opened" to analysis they must be shown to have a signal < 1s. In the end, 99% of the data were available (boxes need not to be exclusive set) Teppei Katori, McGill University HEP seminar

  39. 6. Blind analysis “Intrinsic”ne + nesources: • m+e+nm ne (52%) • K+ p0 e+ne (29%) • K0 p e ne (14%) • Other ( 5%) p m nm Km nm Since MiniBooNE is blind analysis experiment, we need to constraint intrinsic ne background without measuring directly (1) m decay ne background (2) K decay ne background m e nm ne Kp e ne ne/nm = 0.5% Antineutrino content: 6% Teppei Katori, McGill University HEP seminar

  40. 6. Blind analysis CCQE En (GeV) En = 0.43 Ep Ep(GeV) hit time (1) measure nm flux from nmCCQE event to constraint nebackground from m decay nmCCQE is one of the open boxes. Kinematics allows connection topflux, hence intrinsic ne background from m decay is constraint. NC veto hits high energy energy p m nm En-Ep space m e nm ne Teppei Katori, McGill University HEP seminar

  41. 6. Blind analysis CCQE hit time (2) measure high energy nm events to constraint nebackground from K decay At high energies, above “signal range” nm and “ne -like” events are largely due to kaon decay NC veto hits high energy energy p m nm example of open boxes; - nmCCQE - high energy event - CCp+ - NC elastics - NC po - NC electron scattering - Michel electron etc.... Km nm signal range n events Dominated by Kaon decay Kp e ne Teppei Katori, McGill University HEP seminar

  42. 6. MiniBooNE oscillation analysis structure Likelihood Particle ID BoostingParticle ID Start with a GEANT4 flux prediction for the n spectrum from p and K produced at the target Predict n interactions using NUANCE neutrino interaction generator Pass final state particles to GEANT3 to model particle and light propagation in the tank Starting with event reconstruction, independent analyses form: (1) Track Based Likelihood (TBL) and (2) Boosted Decision Tree (BDT) Develop particle ID/cuts to separate signal from background Fit reconstructed EnQE spectrum for oscillations detector model Simultaneous Fit to nm& ne Pre-Normalize to nm ; Fit ne Teppei Katori, McGill University HEP seminar

  43. 6. Track-Based Likelihood (TBL) analysis neCCQE Signal cut MC nmCCQE This algorithm was found to have the better sensitivity tonmneappearance. Therefore, before unblinding, this was the algorithm chosen for the “primary result” Fit event with detailed, direct reconstruction of particle tracks, and ratio of fit likelihoods to identify particle Separating e from m positive (negative) likelihood ratio favors electron (muon) hypothesis Teppei Katori, McGill University HEP seminar

  44. 6. Track-Based Likelihood (TBL) analysis log(Le/Lp) cut neCCQE signal region (blinded) nm NCpo Monte Carlo π0 only reconstructed pomass cut BLIND MC nm NCp0 e po BLIND ne CCQE po e Invariant Mass Separating e from po electron hypothesis is tested in 2 dimensional likelihood ratio space Teppei Katori, McGill University HEP seminar

  45. 6. Track-Based Likelihood (TBL) analysis 475 MeV – 1250 MeV νeK94 νeμ132 π⁰ 62 dirt 17 Δ→Nγ 20 other 33 total 358 LSND best-fit νμ→νe 126 TBL analysis summary - Oscillation analysis uses 475MeV<E<1250MeV Teppei Katori, McGill University HEP seminar

  46. 6. Boosted Decision Tree (BDT) analysis Input variables (~100) Boosting Algorithm Boosted Decision Trees - data learning method (e.g., neural network,...) - ~100 input variables from point like model event reconstruction - combined many weak trees ( ~1000 weak trees) to make strong "committee" - Designed to classify signal and background Signal = oscillationne CCQE events , Background = everything else (misID) Output PID variables Teppei Katori, McGill University HEP seminar

  47. 6. Boosted Decision Tree (BDT) analysis BDT analysis summary - Oscillation analysis uses 300MeV<E<1600MeV - PID cut is defined each EnQE bin PID cut Teppei Katori, McGill University HEP seminar

  48. 7. Systematic error analysis Teppei Katori, McGill University HEP seminar

  49. 7. Error analysis We have two categories of backgrounds: nmmis-id intrinsicne (TB analysis) Teppei Katori, McGill University HEP seminar

  50. 7. Error analysis Handling uncertainties in the analyses: What we begin with... ... what we need For a given source of uncertainty, Errors in bins of EnQE and information on the correlations between bins For a given source of uncertainty, Errors on a wide range of parameters in the underlying model Teppei Katori, McGill University HEP seminar

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