03/07/2007Teppei Katori, Indiana University, PMN07 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE.

03/07/2007Teppei Katori, Indiana University, PMN07 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE collaboration Indiana University PMN07, Blaubeuren, July, 03, 07 PRL98(2007)231801

03/07/2007Teppei Katori, Indiana University, PMN07 2 The first result of the MiniBooNE neutrino oscillation experiment outline 1. Conclusion 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 PRL98(2007)231801

03/07/2007Teppei Katori, Indiana University, PMN07 3 1. Conclusion

03/07/2007Teppei Katori, Indiana University, PMN07 4 The observed reconstructed energy distribution is inconsistent with a   e appearance- only neutrino mass model 1. Conclusion Excluded region MiniBooNE is incompatible with   e appearance only interpretation of LSND at 98% CL

03/07/2007Teppei Katori, Indiana University, PMN07 5 2. LSND experiment

03/07/2007Teppei Katori, Indiana University, PMN07 6 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

03/07/2007Teppei Katori, Indiana University, PMN07 7 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 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 New physics? - sterile neutrino - Lorentz violation - CPT violation - extra dimension - etc

03/07/2007Teppei Katori, Indiana University, PMN07 8 Keep L/E same with LSND, while changing systematics, energy & event signature; P    e  sin   sin   m  L  MiniBooNE has; - higher energy (~500 MeV) than LSND (~30 MeV) - longer baseline (~500 m) than LSND (~30 m) MiniBooNE is looking for the single isolated electron like events, which is the signature of e events 2. MiniBooNE experiment Booster primary beamtertiary beamsecondary beam (protons)(mesons)(neutrinos) K+K+    e   target and horn dirt absorberdetectordecay region FNAL Booster

03/07/2007Teppei Katori, Indiana University, PMN07 9 3. Neutrino beam

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

03/07/2007Teppei Katori, Indiana University, PMN07 11   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    

03/07/2007Teppei Katori, Indiana University, PMN07 12 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)

03/07/2007Teppei Katori, Indiana University, PMN07 13   e   3. Neutrino beam Decay pipe Booster primary beamtertiary beamsecondary beam (protons)(mesons)(neutrinos) K+K+  target and horn dirt absorberdetectordecay regionFNAL Booster 50m decay pipe for pion decay in flight

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

03/07/2007Teppei Katori, Indiana University, PMN07 15 4. Events in the Detector

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

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

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

03/07/2007Teppei Katori, Indiana University, PMN07 22 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

03/07/2007Teppei Katori, Indiana University, PMN07 23 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

03/07/2007Teppei Katori, Indiana University, PMN07 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! 4. Events in the Detector Beam Only

03/07/2007Teppei Katori, Indiana University, PMN07 25 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

03/07/2007Teppei Katori, Indiana University, PMN07 26 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

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

03/07/2007Teppei Katori, Indiana University, PMN07 28 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

03/07/2007Teppei Katori, Indiana University, PMN07 29 5. Cross section model

03/07/2007Teppei Katori, Indiana University, PMN07 30 Predicted event rates before cuts (NUANCE Monte Carlo) Casper, Nucl.Phys.Proc.Suppl. 112 (2002) 161 5. Cross section model More detail at afternoon talk

03/07/2007Teppei Katori, Indiana University, PMN07 31 6. Oscillation analysis

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

03/07/2007Teppei Katori, Indiana University, PMN07 33 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)

03/07/2007Teppei Katori, Indiana University, PMN07 34 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   MC reconstructed    mass cut  NC  0 e CCQE log(L e /L  ) cut  NC  0 e CCQE Separating e from 

03/07/2007Teppei Katori, Indiana University, PMN07 35 6. Track-Based Likelihood (TBL) analysis dirt 17 Δ→ N  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

03/07/2007Teppei Katori, Indiana University, PMN07 36 7. Systematic error analysis

03/07/2007Teppei Katori, Indiana University, PMN07 37 We have two categories of backgrounds: (TB analysis)  mis-id intrinsic e 7. Error analysis many systematic errors are correlated (beam parameters, cross section model parameters, etc) MiniBooNE is very careful to propagate correlated systematic errors...

03/07/2007Teppei Katori, Indiana University, PMN07 38 7. Multisim Oscillation  2 fit for E QE distribution  2 = (data - MC) T (M total ) -1 (data - MC)  + 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 keep the correlation of systematics Output error matrix keep the correlation of E QE bins M total = M(  + production) + M(  - production) + M(K + production) + M(K 0 production) + M(beam model) + M(cross section model) + M(  0 yield) + M(dirt model) + M(detector model) + M(data statistics) B.P.Roe, Nucl.,Instrum.,Meth,A570(2007)157 Multi-simulation method (Multisim)

03/07/2007Teppei Katori, Indiana University, PMN07 39 8. The MiniBooNE initial results

03/07/2007Teppei Katori, Indiana University, PMN07 40 Open the box

03/07/2007Teppei Katori, Indiana University, PMN07 41 TBL analysis TBL show no sign of an excess in the analysis region Visible excess at low E 8. The MiniBooNE initial results BDT (Boosted Decision Tree) analysis BDT has a good fit and no sign of an excess Also sees an excess at low E, but larger normalization error covers it

03/07/2007Teppei Katori, Indiana University, PMN07 42 Energy-fit analysis: solid: TB dashed: BDT Independent analyses are in good agreement. Conclusion: The observed reconstructed energy distribution is inconsistent with a   e appearance-only neutrino mass model 8. The MiniBooNE initial results Excluded region

03/07/2007Teppei Katori, Indiana University, PMN07 43 9. Low energy excess events

03/07/2007Teppei Katori, Indiana University, PMN07 44 Our goals for this first analysis were: (1) An analysis of the data within a   e appearance-only context (2) A generic search for a e excess in our  beam (1) Within the energy range defined by this oscillation analysis, the event rate is consistent with background. 9. Excess at low energy region? (2) The observed low energy deviation is under investigation, including the possibility of more exotic model - Lorentz violation - 3+2 sterile neutrino - extra dimension etc...

03/07/2007Teppei Katori, Indiana University, PMN07 45 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

03/07/2007Teppei Katori, Indiana University, PMN07 46 A paper is accepted by PRL, PRL98(2007)231801 Many more papers supporting this analysis will follow, in the very near future:  CCQE production, hep-ex/0706.0926, submitted to PRL   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. 10. Future plans

03/07/2007Teppei Katori, Indiana University, PMN07 47 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!

03/07/2007Teppei Katori, Indiana University, PMN07 48 10. Back up

03/07/2007Teppei Katori, Indiana University, PMN07 49 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.

03/07/2007Teppei Katori, Indiana University, PMN07 50 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

03/07/2007Teppei Katori, Indiana University, PMN07 51 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

03/07/2007Teppei Katori, Indiana University, PMN07 52 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

03/07/2007Teppei Katori, Indiana University, PMN07 53 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

03/07/2007Teppei Katori, Indiana University, PMN07 54 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

03/07/2007Teppei Katori, Indiana University, PMN07 55 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

03/07/2007Teppei Katori, Indiana University, PMN07 56 Observed and expected events per minute Full Run 3. Stability of running

03/07/2007Teppei Katori, Indiana University, PMN07 57 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

03/07/2007Teppei Katori, Indiana University, PMN07 58 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

03/07/2007Teppei Katori, Indiana University, PMN07 59 4. Calibration source Muon tracker and scintillation cube system Laser flask system

03/07/2007Teppei Katori, Indiana University, PMN07 60 4. Calibration source

03/07/2007Teppei Katori, Indiana University, PMN07 61 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

03/07/2007Teppei Katori, Indiana University, PMN07 62 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

03/07/2007Teppei Katori, Indiana University, PMN07 63 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

03/07/2007Teppei Katori, Indiana University, PMN07 64 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

03/07/2007Teppei Katori, Indiana University, PMN07 65 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

03/07/2007Teppei Katori, Indiana University, PMN07 66 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

03/07/2007Teppei Katori, Indiana University, PMN07 67 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

03/07/2007Teppei Katori, Indiana University, PMN07 68 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

03/07/2007Teppei Katori, Indiana University, PMN07 69 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

03/07/2007Teppei Katori, Indiana University, PMN07 70 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

03/07/2007Teppei Katori, Indiana University, PMN07 71 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

03/07/2007Teppei Katori, Indiana University, PMN07 72 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  0 e CCQE e  Invariant Mass e  BLIND Monte Carlo π 0 only signal region (blinded) BLIND

03/07/2007Teppei Katori, Indiana University, PMN07 73 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

03/07/2007Teppei Katori, Indiana University, PMN07 74 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

03/07/2007Teppei Katori, Indiana University, PMN07 75 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

03/07/2007Teppei Katori, Indiana University, PMN07 76 (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

03/07/2007Teppei Katori, Indiana University, PMN07 77 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

03/07/2007Teppei Katori, Indiana University, PMN07 78 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

03/07/2007Teppei Katori, Indiana University, PMN07 79 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

03/07/2007Teppei Katori, Indiana University, PMN07 80 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

03/07/2007Teppei Katori, Indiana University, PMN07 81 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

03/07/2007Teppei Katori, Indiana University, PMN07 82 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 B.P.Roe, Nucl.,Instrum.,Meth,A570(2007)157

03/07/2007Teppei Katori, Indiana University, PMN07 83 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

03/07/2007Teppei Katori, Indiana University, PMN07 84 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

03/07/2007Teppei Katori, Indiana University, PMN07 85 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)

03/07/2007Teppei Katori, Indiana University, PMN07 86 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

03/07/2007Teppei Katori, Indiana University, PMN07 87 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

03/07/2007Teppei Katori, Indiana University, PMN07 88 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

03/07/2007Teppei Katori, Indiana University, PMN07 89 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

03/07/2007Teppei Katori, Indiana University, PMN07 90 “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

03/07/2007Teppei Katori, Indiana University, PMN07 91 Summary of predicted backgrounds for the final MiniBooNE result (Track Based Analysis): (example signal) 7. Error analysis

03/07/2007Teppei Katori, Indiana University, PMN07 92 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

03/07/2007Teppei Katori, Indiana University, PMN07 93 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

03/07/2007Teppei Katori, Indiana University, PMN07 94 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

03/07/2007Teppei Katori, Indiana University, PMN07 95 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

03/07/2007Teppei Katori, Indiana University, PMN07 96 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

03/07/2007Teppei Katori, Indiana University, PMN07 97 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

03/07/2007Teppei Katori, Indiana University, PMN07 98 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

03/07/2007Teppei Katori, Indiana University, PMN07 99 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

03/07/2007Teppei Katori, Indiana University, PMN07 100 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

03/07/2007Teppei Katori, Indiana University, PMN07 101 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

03/07/2007Teppei Katori, Indiana University, PMN07 102 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...

03/07/2007Teppei Katori, Indiana University, PMN07 103 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...

03/07/2007Teppei Katori, Indiana University, PMN07 104 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...

03/07/2007Teppei Katori, Indiana University, PMN07 105 9. Lorentz violation model Under the active (particle) Lorentz Transformation; y x

03/07/2007Teppei Katori, Indiana University, PMN07 106 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;

03/07/2007Teppei Katori, Indiana University, PMN07 107 9. Lorentz violation model Under the passive (observer) Lorentz Transformation; y x

03/07/2007Teppei Katori, Indiana University, PMN07 108 9. Lorentz violation model y x Under the passive (observer) Lorentz Transformation;

03/07/2007Teppei Katori, Indiana University, PMN07 109 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;

03/07/2007Teppei Katori, Indiana University, PMN07 110 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)

03/07/2007Teppei Katori, Indiana University, PMN07 111 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

03/07/2007Teppei Katori, Indiana University, PMN07 112 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

03/07/2007Teppei Katori, Indiana University, PMN07 113 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)

03/07/2007Teppei Katori, Indiana University, PMN07 116 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!)

03/07/2007Teppei Katori, Indiana University, PMN07 117 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

03/07/2007Teppei Katori, Indiana University, PMN07 118 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

03/07/2007Teppei Katori, Indiana University, PMN07 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE.

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03/07/2007Teppei Katori, Indiana University, PMN07 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE.

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Presentation on theme: "03/07/2007Teppei Katori, Indiana University, PMN07 1 The first result of the MiniBooNE neutrino oscillation experiment Teppei Katori for the MiniBooNE."— Presentation transcript:

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