2. 05/09/2007Teppei Katori, McGill University HEP seminar 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE collaboration Indiana University McGill University, Montreal, May., 09, 07

3. 05/09/2007Teppei Katori, McGill University HEP seminar 2 The first result of the MiniBooNE neutrino oscillation experiment outline 1. Neutrino oscillation 2. LSND experiment 3. Neutrino beam 4. Events in the detector 5. Cross section model 6. Oscillation analysis 7. Systematic error analysis 8. The MiniBooNE initial results 9. Low energy excess events 10. Future plans

4. 05/09/2007Teppei Katori, McGill University HEP seminar 3 1. Neutrino Oscillation

5. 05/09/2007Teppei Katori, McGill University HEP seminar 4 1. Neutrino oscillation The neutrino weak eigenstate is described by neutrino Hamiltonian eigenstates, 1, 2, and 3 and their mixing matrix elements. Then the transition probability from weak eigenstate  to e is The time evolution of neutrino weak eigenstate is written by Hamiltonian mixing matrix elements and eigenvalues of 1, 2, and 3. So far, model independent

6. 05/09/2007Teppei 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

7. 05/09/2007Teppei Katori, McGill University HEP seminar 6 2. LSND experiment

8. 05/09/2007Teppei Katori, McGill University HEP seminar 7 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.  ) LSND signal LSND Collaboration, PRD 64, 112007 L/E~30m/30MeV~1

9. 05/09/2007Teppei Katori, McGill University HEP seminar 8 3 types of neutrino oscillations are found: LSND neutrino oscillation:  m 2 ~1eV 2 Atmospheric neutrino oscillation:  m 2 ~10 -3 eV 2 Solar neutrino oscillation :  m 2 ~10 -5 eV 2 But we cannot have so many  m 2 ! 2. LSND experiment  m 13 2 =  m 12 2 +  m 23 2 increasing (mass) 2 We need to test LSND signal MiniBooNE experiment is designed to have same L/E~500m/500MeV~1 to test LSND  m 2 ~1eV 2

10. 05/09/2007Teppei Katori, McGill University HEP seminar 9 Keep L/E same with LSND, while changing systematics, energy & event signature; P    e  sin   sin   m  L  MiniBooNE is looking for the single isolated electron like events, which is the signature of e events MiniBooNE has; - higher energy (~500 MeV) than LSND (~30 MeV) - longer baseline (~500 m) than LSND (~30 m) 2. MiniBooNE experiment Booster primary beamtertiary beamsecondary beam (protons)(mesons)(neutrinos) K+K+    e   target and horn dirt absorberdetectordecay region FNAL Booster

11. 05/09/2007Teppei Katori, McGill University HEP seminar 10 3. Neutrino beam

12. 05/09/2007Teppei Katori, McGill University HEP seminar 11 Booster Target Hall MiniBooNE extracts beam from the 8 GeV Booster 3. Neutrino beam Booster K+K+ target and horndetector dirt absorber primary beamtertiary beamsecondary beam (protons)(mesons)(neutrinos)    e   decay region FNAL Booster

13. 05/09/2007Teppei Katori, McGill University HEP seminar 12   e   within a magnetic horn (2.5 kV, 174 kA) that (increases the flux by  6) 3. Neutrino beam 8GeV protons are delivered to a 1.7  Be target Magnetic focusing horn Booster primary beamtertiary beamsecondary beam (protons)(mesons)(neutrinos) K+K+  target and horn dirt absorberdetectordecay regionFNAL Booster    

14. 05/09/2007Teppei Katori, McGill University HEP seminar 13 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)

15. 05/09/2007Teppei Katori, McGill University HEP seminar 14   e   e K   e e K       Neutrino Flux from GEANT4 Simulation MiniBooNE is the e appearance oscillation experiment “Intrinsic” e +  e sources:    e +    e (52%)  K +    e + e (29%)  K 0   e e (14%)  Other ( 5%) e /  = 0.5% Antineutrino content: 6% 3. Neutrino beam

16. 05/09/2007Teppei Katori, McGill University HEP seminar 15 4. Events in the Detector

17. 05/09/2007Teppei Katori, McGill University HEP seminar 16 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector

18. 05/09/2007Teppei Katori, McGill University HEP seminar 17 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector Booster 541 meters

19. 05/09/2007Teppei Katori, McGill University HEP seminar 18 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector

20. 05/09/2007Teppei Katori, McGill University HEP seminar 19 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector

21. 05/09/2007Teppei Katori, McGill University HEP seminar 20 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector Extinction rate of MiniBooNE oil

22. 05/09/2007Teppei Katori, McGill University HEP seminar 21 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 (CH 2 ) (Fiducial volume: 450 t) - 1280 inner phototubes, - 240 veto phototubes Simulated with a GEANT3 Monte Carlo 4. Events in the Detector

23. 05/09/2007Teppei Katori, McGill University HEP seminar 22 4. Events in the Detector  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

24. 05/09/2007Teppei Katori, McGill University HEP seminar 23 19.2  s beam trigger window with the 1.6  s spill Multiple hits within a ~100 ns window form “subevents”  CCQE interactions ( +n   +p) with characteristic two “subevent” structure from stopped   e e  e Number of tank hits for CCQE event 4. Events in the Detector

25. 05/09/2007Teppei Katori, McGill University HEP seminar 24 Times of hit-clusters (subevents) Beam spill (1.6  s) 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 4. Events in the Detector

26. 05/09/2007Teppei Katori, McGill University HEP seminar 25 Times of hit-clusters (subevents) Beam spill (1.6  s) 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! 4. Events in the Detector Beam and Michels

27. 05/09/2007Teppei Katori, McGill University HEP seminar 26 Times of hit-clusters (subevents) Beam spill (1.6  s) 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! 4. Events in the Detector Beam Only

28. 05/09/2007Teppei Katori, McGill University HEP seminar 27 Muons – Sharp, clear rings Long, straight tracks Electrons – Scattered rings Multiple scattering Radiative processes Neutral Pions – Double rings Decays to two photons 4. Events in the Detector

29. 05/09/2007Teppei 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

30. 05/09/2007Teppei Katori, McGill University HEP seminar 29 Muons – Sharp, clear rings Long, straight tracks Electrons – Scattered rings Multiple scattering Radiative processes Neutral Pions – Double rings Decays to two photons 4. Events in the Detector

31. 05/09/2007Teppei Katori, McGill University HEP seminar 30 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) 4. Events in the Detector

32. 05/09/2007Teppei Katori, McGill University HEP seminar 31 5. Cross section model

33. 05/09/2007Teppei Katori, McGill University HEP seminar 32 Predicted event rates before cuts (NUANCE Monte Carlo) Casper, Nucl.Phys.Proc.Suppl. 112 (2002) 161 Event neutrino energy (GeV) 5. Cross section model

34. 05/09/2007Teppei Katori, McGill University HEP seminar 33 CCQE (Charged Current Quasi-Elastic) - 39% of total - Events are “clean” (few particles) - Energy of the neutrino (E QE ) can be reconstructed from; - Scattering angle cos  - Visible energy E visible 5. Cross section model  12 C -beam cos  E visible

35. 05/09/2007Teppei Katori, McGill University HEP seminar 34 The data-MC agreement in Q 2 (4-momentum transfer) distribution is not great We tuned nuclear parameters in Relativistic Fermi Gas model 5. Cross section model Q 2 fits to MB  CCQE data using the nuclear parameters: M A eff - effective axial mass E lo SF - Pauli Blocking parameter Relativistic Fermi Gas Model with tuned parameters describes  CCQE data well (paper in preparation) Q 2 distribution before and after fitting Smith and Moniz, Nucl.,Phys.,B43(1972)605

36. 05/09/2007Teppei Katori, McGill University HEP seminar 35 5. Cross section model Q 2 =0.2GeV 2 Q 2 =0.6GeV 2 Q 2 =1.0GeV 2 Q 2 =2.0GeV 2 E = 0.3GeV 0.6GeV 1.0GeV 2.0GeV data-MC ratio after the fit data-MC ratio is flat through entire kinematic space made by CCQE interaction

37. 05/09/2007Teppei Katori, McGill University HEP seminar 36 6. Oscillation analysis

38. 05/09/2007Teppei Katori, McGill University HEP seminar 37 The MiniBooNE signal is small but relatively easy to isolate The data is described in n-dimensional space; 6. Blind analysis hit time energy veto hits

39. 05/09/2007Teppei Katori, McGill University HEP seminar 38 The MiniBooNE signal is small but relatively easy to isolate The data is described in n-dimensional space; 6. Blind analysis hit time energy veto hits CCQE NC high energy The data is classified into "box". For boxes to be "opened" to analysis they must be shown to have a signal < 1  In the end, 99% of the data were available (boxes need not to be exclusive set) e candidate (closed box)

40. 05/09/2007Teppei Katori, McGill University HEP seminar 39 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

41. 05/09/2007Teppei Katori, McGill University HEP seminar 40 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

42. 05/09/2007Teppei Katori, McGill University HEP seminar 41 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

43. 05/09/2007Teppei Katori, McGill University HEP seminar 42 6. MiniBooNE oscillation analysis structure Start with a GEANT4 flux prediction for the spectrum from  and K produced at the target Predict 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 E QE spectrum for oscillations detector model BoostingP article ID Likelihood Particle ID Simultaneous Fit to   & e Pre-Normalize to   Fit e

44. 05/09/2007Teppei Katori, McGill University HEP seminar 43 6. Track-Based Likelihood (TBL) analysis e CCQE  CCQE MC Signal cut This algorithm was found to have the better sensitivity to   e appearance. 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  positive (negative) likelihood ratio favors electron (muon) hypothesis

45. 05/09/2007Teppei Katori, McGill University HEP seminar 44 Separating e from   electron hypothesis is tested in 2 dimensional likelihood ratio space 6. Track-Based Likelihood (TBL) analysis MC reconstructed    mass cut  NC  0 e CCQE log(L e /L  ) cut  NC   e CCQE e  Invariant Mass e  BLIND Monte Carlo π 0 only signal region (blinded) BLIND

46. 05/09/2007Teppei Katori, McGill University HEP seminar 45 6. Track-Based Likelihood (TBL) analysis dirt 17 Δ→ N γ 20 ν e K 94 ν e μ 132 π ⁰ 62 475 MeV – 1250 MeV other 33 total 358 LSND best-fit ν μ →ν e 126 TBL analysis summary - Oscillation analysis uses 475MeV<E<1250MeV

47. 05/09/2007Teppei Katori, McGill University HEP seminar 46 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 = oscillation e CCQE events, Background = everything else (misID) Boosting Algorithm Output PID variables Input variables (~100) 6. Boosted Decision Tree (BDT) analysis

48. 05/09/2007Teppei Katori, McGill University HEP seminar 47 6. Boosted Decision Tree (BDT) analysis PID cut BDT analysis summary - Oscillation analysis uses 300MeV<E<1600MeV - PID cut is defined each E QE bin

49. 05/09/2007Teppei Katori, McGill University HEP seminar 48 7. Systematic error analysis

50. 05/09/2007Teppei Katori, McGill University HEP seminar 49 We have two categories of backgrounds: (TB analysis)  mis-id intrinsic e 7. Error analysis

51. 05/09/2007Teppei Katori, McGill University HEP seminar 50 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

52. 05/09/2007Teppei Katori, McGill University HEP seminar 51 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

53. 05/09/2007Teppei Katori, McGill University HEP seminar 52 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 (27 parameters)  0 yield (9 parameters) dirt model (1 parameters) detector model (39 parameters) dependent independent Input error matrices Roe et al., Nucl.,Phys.,B43(1972)605

54. 05/09/2007Teppei Katori, McGill University HEP seminar 53 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

55. 05/09/2007Teppei Katori, McGill University HEP seminar 54 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

56. 05/09/2007Teppei Katori, McGill University HEP seminar 55 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)

57. 05/09/2007Teppei Katori, McGill University HEP seminar 56 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

58. 05/09/2007Teppei Katori, McGill University HEP seminar 57 7. Multisim Total output error matrix M total = M(p + production) + M(p - production) + M(K + production) + M(K 0 production) + M(beamline model) + M(cross section model) + M(p 0 yield) + M(dirt model) + M(detector model) + M(data stat) Oscillation analysis  2 fit  2 = (data - MC) T (M total ) -1 (data - MC)

59. 05/09/2007Teppei Katori, McGill University HEP seminar 58 8. The MiniBooNE initial results

60. 05/09/2007Teppei Katori, McGill University HEP seminar 59 After applying all analysis cuts: 1. Blind  2 test for a set of diagnostic variables. 2. Open up the plots from step 1. 3. Blind  2 test for a fit to E QE, without returning fit parameters. 4. Compare E QE in data and Monte Carlo, returning the fit parameters. box opening (March 26, 2007) 8. Box opening procedure

61. 05/09/2007Teppei Katori, McGill University HEP seminar 60 12 variables are tested for TBL 46 variables are tested for BDT All analysis variables were returned with good probability except... Track Based Likelihood analysis  2 probability of E visible fit is 1% We change the energy cut >475MeV for oscillation fit 8. Step 1. Blind  2 test

62. 05/09/2007Teppei Katori, McGill University HEP seminar 61 MC contains fitted signal at unknown level TBL (E QE >475 MeV) BDT 8. Step 2. Open the blind  2 test plots Opening 12 plots for TBL and 46 plots for BDT fitted energy (MeV) E visible  2 Prob= 28%  2 Prob= 59% ex) Visible energy

63. 05/09/2007Teppei Katori, McGill University HEP seminar 62 This is the  2 oscillation fit TBL (E QE >475 MeV)  2 Probability of fit: 99% BDT analysis  2 Probability of fit: 52% Step 4. Open the box... 8. Step 3. Blind  2 test for a fit to E QE

64. 05/09/2007Teppei Katori, McGill University HEP seminar 63 Step 4. Open the box

65. 05/09/2007Teppei Katori, McGill University HEP seminar 64 TBL analysis TBL show no sign of an excess in the analysis region (where the LSND signal is expected from 1 sterile neutrino interpretation) Visible excess at low E 8. The MiniBooNE initial results BDT analysis BDT has a good fit and no sign of an excess, in fact the data is low relative to the prediction Also sees an excess at low E, but larger normalization error covers it

66. 05/09/2007Teppei Katori, McGill University HEP seminar 65 BDT analysis Counting Experiment: 300<E QE <1600 MeV data: 971 events expectation: 1070  33 (stat)  225 (sys) events significance:  0.38  TBL analysis Counting Experiment: 475<E QE <1250 MeV data: 380 events expectation: 358  19 (stat)  35 (sys) events significance: 0.55  8. The MiniBooNE initial results

67. 05/09/2007Teppei Katori, McGill University HEP seminar 66 Energy-fit analysis: solid: TB dashed: BDT Independent analyses are in good agreement. The observed reconstructed energy distribution is inconsistent with a   e appearance-only model 8. The MiniBooNE initial results Excluded region

68. 05/09/2007Teppei Katori, McGill University HEP seminar 67 9. Low energy excess events

69. 05/09/2007Teppei Katori, McGill University HEP seminar 68 Our goals for this first analysis were: - A generic search for a e excess in our  beam, - An analysis of the data within a   e appearance-only context Within the energy range defined by this oscillation analysis, the event rate is consistent with background. 9. Excess at low energy region? The observed low energy deviation is under investigation, including the possibility of more exotic model - Lorentz violation - 3+2 sterile neutrino - extra dimension etc...

70. 05/09/2007Teppei Katori, McGill University HEP seminar 69 9. Lorentz violation model Science American (Sept. 2004) aa vacuum Hamiltonian for fermion Lorentz and CPT violation is a predicted phenomenon from Planck scale physics Characteristic signals of Lorentz violation in neutrino oscillation physics is... 1. sidereal variation of oscillation data 2. anomalous energy dependence 3. CPT violation etc...

71. 05/09/2007Teppei Katori, McGill University HEP seminar 70 9. Lorentz violation model Science American (Sept. 2004) aa aa SSB vacuum Hamiltonian for fermion Lorentz and CPT violation is a predicted phenomenon from Planck scale physics Characteristic signals of Lorentz violation in neutrino oscillation physics is... 1. sidereal variation of oscillation data 2. anomalous energy dependence 3. CPT violation etc...

72. 05/09/2007Teppei Katori, McGill University HEP seminar 71 9. Lorentz violation model Science American (Sept. 2004) aa aa SSB vacuum Hamiltonian for fermion background field of the universe Lorentz and CPT violation is a predicted phenomenon from Planck scale physics Characteristic signals of Lorentz violation in neutrino oscillation physics is... 1. sidereal variation of oscillation data 2. anomalous energy dependence 3. CPT violation etc...

73. 05/09/2007Teppei Katori, McGill University HEP seminar 72 9. Lorentz violation model Under the active (particle) Lorentz Transformation; y x

74. 05/09/2007Teppei Katori, McGill University HEP seminar 73 9. Lorentz violation model by definition, "a" is insensitive to active transformation y x Lorentz violation can be seen in fixed coordinate system Physics (ex. neutrino oscillation) depends on the sidereal motion Under the active (particle) Lorentz Transformation;

75. 05/09/2007Teppei Katori, McGill University HEP seminar 74 9. Lorentz violation model Under the passive (observer) Lorentz Transformation; y x

76. 05/09/2007Teppei Katori, McGill University HEP seminar 75 9. Lorentz violation model y x Under the passive (observer) Lorentz Transformation;

77. 05/09/2007Teppei Katori, McGill University HEP seminar 76 9. Lorentz violation model y x Lorentz violation cannot be seen by observers motion (coordinate transformation is unbroken) Physics doesn't depend on observers motion Under the passive (observer) Lorentz Transformation;

78. 05/09/2007Teppei Katori, McGill University HEP seminar 77 9. Lorentz violation model Neutrino oscillation sidereal time dependence Kostelecky and Mewes PRD70(2004)076002 sidereal frequency sidereal time Lorentz violation fit for LSND TK and LSND collaboration, PRD72(2005)076004 LSND neutrino oscillation data is consistent with no Lorentz violation (but cannot rule out)

79. 05/09/2007Teppei Katori, McGill University HEP seminar 78 9. Lorentz violation model TK et al, PRD74(2006)105009 This simple model is consistent with all existing neutrino oscillation data, including solar, atmospheric, reactor, and LSND. It is possible to construct the global model for neutrino oscillation including Lorentz and CPT violation effective Hamiltonian m 2 = 1.04  10 -3 eV 2 : mass term a = -2.4  10 -19 GeV : Lorentz and CPT violation term c = 3.4  10 -17 : Lorentz violation term

80. 05/09/2007Teppei Katori, McGill University HEP seminar 79 9. Lorentz violation model TK et al, PRD74(2006)105009 This simple model is consistent with all existing neutrino oscillation data, including solar, atmospheric, reactor, and LSND. It predicts signal at low E region for MiniBooNE Fit with Lorentz violation model is under study. Theoretical signals (no experimental smearing) effective Hamiltonian It is possible to construct the global model for neutrino oscillation including Lorentz and CPT violation

81. 05/09/2007Teppei Katori, McGill University HEP seminar 80 10. Future plans

82. 05/09/2007Teppei Katori, McGill University HEP seminar 81 A paper is submitted to PRL. Many more papers supporting this analysis will follow, in the very near future:  CCQE production   production MiniBooNE-LSND-Karmen joint analysis We are pursuing further analyses of the neutrino data, including... an analysis which combines TBL and BDT more exotic model for the LSND effect MiniBooNE is presently taking data in antineutrino mode. Further questions of MiniBooNE are answered by SciBooNE including... cross check of intrinsic e measurement reduce cross section uncertainty reduce  + flux uncertainty etc... 10. Future plans What is SciBooNE?

83. 05/09/2007Teppei Katori, McGill University HEP seminar 82 DIS QE 11 10. 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

84. 05/09/2007Teppei Katori, McGill University HEP seminar 83 10. 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

85. 05/09/2007Teppei Katori, McGill University HEP seminar 84 10. 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 

86. 05/09/2007Teppei Katori, McGill University HEP seminar 85 10. SciBooNE SciBar installation

87. 05/09/2007Teppei Katori, McGill University HEP seminar 86 10. SciBooNE Stay tuned... MRD installation

88. 05/09/2007Teppei Katori, McGill University HEP seminar 87 BooNE 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!

89. 05/09/2007Teppei Katori, McGill University HEP seminar 88 10. Back up

90. 05/09/2007Teppei Katori, McGill University HEP seminar 89 1. Neutrino oscillation Neutrino oscillation is a quantum interference experiment (cf. double slit experiment). 1 2 If 2 Hamiltonian eigenstates, 1 and 2 have different phase rotation, they cause quantum interference. For double slit experiment, if path 1 and path 2 have different length, they have different phase rotations and it causes interference.

91. 05/09/2007Teppei Katori, McGill University HEP seminar 90 1. Neutrino oscillation Neutrino oscillation is a quantum interference experiment (cf. double slit experiment). 1 2 If 2 neutrino Hamiltonian eigenstates, 1 and 2, have different phase rotation, they cause quantum interference. For massive neutrinos, if 1 is heavier than 2, they have different group velocities hence different phase rotation, thus the superposition of those 2 wave packet no longer makes same state as before  e U  UeUe 1 2

92. 05/09/2007Teppei Katori, McGill University HEP seminar 91 2. LSND experiment In terms of the oscillation probability, P(    e )=0.264 ± 0.067 ± 0.045  e disapp.    e Under the 2 flavor massive neutrino oscillation model, one can map into  m 2 -sin 2 2  space (MS-diagram) This model allows comparison to other experiments: Karmen2 Bugey

93. 05/09/2007Teppei Katori, McGill University HEP seminar 92 4  10 12 protons per 1.6  s pulse delivered at up to 5 Hz. 5.58  10 20 POT (proton on target) 20  s 1.6  s Beam macro structure 19ns 1.5 ns Beam micro structure 3. Neutrino beam FNAL Booster Booster Target Hall

94. 05/09/2007Teppei Katori, McGill University HEP seminar 93 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

95. 05/09/2007Teppei Katori, McGill University HEP seminar 94 Observed and expected events per minute Full Run 3. Stability of running

96. 05/09/2007Teppei Katori, McGill University HEP seminar 95 4. Calibration source Muon tracker and scintillation cube system Laser flask system

97. 05/09/2007Teppei Katori, McGill University HEP seminar 96 4. Calibration source

98. 05/09/2007Teppei Katori, McGill University HEP seminar 97 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

99. 05/09/2007Teppei Katori, McGill University HEP seminar 98 Both Algorithms and all analyses presented here share “hit-level pre-cuts”: Veto hits < 6 Tank hits > 200 Only 1 subevent And a radius precut: R<500 cm (where reconstructed R is algorithm-dependent) data MC 6. Precuts

100. 05/09/2007Teppei Katori, McGill University HEP seminar 99 This algorithm was found to have the better sensitivity to   e appearance. 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 Fit event under the different hypotheses; - muon like - electron like Fit is characterized by 7 parameters Fit knows - scintillation, Cherenkov light fraction - wave length dependent of light propagation - scattering, reemission, reflection, etc - PMT efficiencies 6. Track-Based Likelihood (TBL) analysis  E t,x,y,z light

101. 05/09/2007Teppei Katori, McGill University HEP seminar 100 Events are reconstructed with point-like model Construct a set of analysis variables (vertex, track length, time cluster, particle direction, event topology, energy, etc) 6. Boosted Decision Tree (BDT) analysis Muon decay electron spectrum  {(x k, y k, z k ), t k, Q k } rkrk (x, y, z, t) (ux, uy, uz) dt k = t k – r k /c n - t Point-like model cc s

102. 05/09/2007Teppei Katori, McGill University HEP seminar 101 (sequential series of cuts based on MC study) A Decision Tree (N signal /N bkgd ) 30,245/16,305 9755/2369 5 20455/3417 9790/12888 1906/11828 7849/11867 signal-like bkgd-like sig-like bkgd-like etc. This tree is one of many possibilities... Variable 1 Variable 2 Variable 3 6. Boosted Decision Tree (BDT) analysis

103. 05/09/2007Teppei Katori, McGill University HEP seminar 102 Leaf Node k th decision tree - a kind of data learning method (e.g., neural network,...) - training sample (MC simulation) is used to train the code - combined many weak classifiers ( ~1000 weak trees) to make strong "committee" Boosted Decision Trees Boosted Decision Tree (k-2) th (k-1) th (k+1) th 6. Boosted Decision Tree (BDT) analysis

104. 05/09/2007Teppei Katori, McGill University HEP seminar 103 Fake data sample The goal of the classifier is to separate blue (signal) and red (background) populations. 2) Many weak trees (single cut trees) only 4 trees shown 1) Development of a single decision tree Two ways to use decision trees. 1) Multiple cuts on X and Y in a big tree, 2) Many weak trees (single-cut trees) combined 1 cut2 cuts 3 cuts 4 cuts Tree 1Tree 2 Tree 3 Tree 4 Example of classification problem 6. Boosted Decision Tree (BDT) analysis

105. 05/09/2007Teppei Katori, McGill University HEP seminar 104 Single decision tree 500 weak trees committee Boosting Algorithm has all the advantages of single decision trees, and less suceptibility to overtraining. Classified as signal Classified as background 6. Boosted Decision Tree (BDT) analysis

106. 05/09/2007Teppei Katori, McGill University HEP seminar 105 TB In Boosted Decision Tree analysis: Low energy bin (200<E QE <300 MeV) constrains  mis-ids:  0,  N , dirt... signal range signal up to 3000 MeV BDT In both analyses, high energy bins constrain e background Use of low-signal/high background energy bins 7. Error analysis

107. 05/09/2007Teppei Katori, McGill University HEP seminar 106 We constrain  0 production using data from our detector Because this constrains the  resonance rate, it also constrains the rate of  N  Reweighting improves agreement in other variables This reduces the error on predicted mis-identified  0 s 7. Error analysis

108. 05/09/2007Teppei Katori, McGill University HEP seminar 107 Other Single Photon Sources From Efrosinin, hep-ph/0609169, calculation checked by Goldman, LANL Neutral Current: + N  + N +  Charged Current + N   + N’ +  negligible where the presence of the  leads to mis-identification Use events where the  is tagged by the michel e -, study misidentification using BDT algorithm. < 6 events @ 95% CL 7. Error analysis

109. 05/09/2007Teppei Katori, McGill University HEP seminar 108 “Dirt” Events Event Type of Dirt after PID cuts Enhanced Background Cuts interactions outside of the detector N data /N MC = 0.99 ± 0.15 Cosmic Rays: Measured from out-of-beam data: 2.1 ± 0.5 events External Sources of Background 7. Error analysis

110. 05/09/2007Teppei Katori, McGill University HEP seminar 109 Summary of predicted backgrounds for the final MiniBooNE result (Track Based Analysis): (example signal) 7. Error analysis

111. 05/09/2007Teppei Katori, McGill University HEP seminar 110 How the constraints enter... TB: Reweight MC prediction to match measured  result (accounting for systematic error correlations) Two Approaches Systematic (and statistical) uncertainties are included in (M ij ) -1 BDT: include the correlations of  to e in the error matrix: (i,j are bins of E QE ) 7. Multisim

112. 05/09/2007Teppei Katori, McGill University HEP seminar 111 Correlations between E QE bins from the optical model: N is number of events passing cuts MC is standard monte carlo  represents a given multisim M is the total number of multisims i,j are E QE bins Error Matrix Elements: Total error matrix is sum from each source. TB: e -only total error matrix BDT:  - e total error matrix MC BDT 7. Multisim

113. 05/09/2007Teppei Katori, McGill University HEP seminar 112 For each error source, “Multisims” are generated by 2 different methods; (1) Event weight multisim: standard Monte Carlo is reweighted. (2) Generated multisim: hit-level simulations are used. number of multisims Number of events passing cuts in bin 500<E QE <600 MeV 1000 event weighted multisims for K + production 70 generated multisims for detector Model 7. Multisim number of multisims

114. 05/09/2007Teppei Katori, McGill University HEP seminar 113 1) There are various ways to present limits: Single sided raster scan (historically used, presented here) Global scan Unified approach (most recent method) 2) This result must be folded into an LSND-Karmen joint analysis. We will present a full joint analysis soon. Two points on interpreting our limit Church, et al., PRD 66, 013001 8. the MiniBooNE initial results

115. 05/09/2007Teppei Katori, McGill University HEP seminar 114 Maximum Joint Probability  m 2 (eV 2 ) MiniBooNE is incompatible with   e appearance only interpretation of LSND at 98% CL 8. the MiniBooNE initial results MiniBooNE-LSND combined fit

116. 05/09/2007Teppei Katori, McGill University HEP seminar 115 9. Lorentz violation model In a field theory, Spontaneous Symmetry Breaking (SSB) create nonzero expectation value for fields (e.g., Higgs) in a true vacuum In string theory, there is a possibility that some Lorentz tensor fields get the nonzero vacuum expectation value (Lorentz symmetry is spontaneously broken) Science American (Sept. 2004) aa aa SSB vacuum Hamiltonian for fermion background field of the universe Kostelecky and Samuel PRD15(1989)683

117. 05/09/2007Teppei Katori, McGill University HEP seminar 116 9. Lorentz violation model PRD74(2006)105009 This model is consistent with all existing neutrino oscillation data, including solar, atmospheric, reactor, and LSND. Lorentz and CPT violating effective Hamiltonian Theoretical signals (no experimental smearing)

118. 05/09/2007Teppei Katori, McGill University HEP seminar 117 9. Lorentz violation model PRD74(2006)105009 This model is consistent with all existing neutrino oscillation data, including solar, atmospheric, reactor, and LSND. Lorentz and CPT violating effective Hamiltonian Theoretical signals (no experimental smearing)

119. 05/09/2007Teppei Katori, McGill University HEP seminar 118 9. Lorentz violation model PRD74(2006)105009 This model is consistent with all existing neutrino oscillation data, including solar, atmospheric, reactor, and LSND. Lorentz and CPT violating effective Hamiltonian Theoretical signals (no experimental smearing)

120. 05/09/2007Teppei Katori, McGill University HEP seminar 119 9. Lorentz violation model Modified Dirac Equation (MDE) SME parameters Standard Model Extension (SME) self-consistent low-energy effective theory with Lorentz and CPT violation within conventional QM (minimum extension of QFT for Particle Lorentz violation) Colladay and Kostelecky ('97) 4X4 Lorentz indices 6X6 flavor indices Direction dependence Hamiltonian with SME parameters has direction dependent physics, so, it is important to fix the coordinate system to describe the effect (Lorentz violating physics is consistent with any frames!)

121. 05/09/2007Teppei Katori, McGill University HEP seminar 120 9. Lorentz violation model usual Hamiltonian (3X3) Short Baseline Approximation good approximation for small oscillation probability experiment (LSND) Sidereal variation is described with 5 coupled SME parameters (combination of a L and c L ) sidereal frequency sidereal time Kostelecky and Mewes ('03) Lorentz violating terms (3X3) Effective Hamiltonian for neutrino oscillation

122. 05/09/2007Teppei Katori, McGill University HEP seminar 121 9. Lorentz violation model Fit the sidereal distribution of 186 candidate events (oscillation and background signals) with the equation of sidereal depending oscillation probability coordinate dependent direction vector Goal