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Zelimir Djurcic Physics Department Columbia University

Status of MiniBooNE Experiment. Zelimir Djurcic Physics Department Columbia University. WIN07, Calcutta January 15-20,2007. MiniBooNE Collaboration. Y. Liu, D.Perevalov, I. Stancu Alabama S. Koutsoliotas Bucknell R.A. Johnson, J.L. Raaf Cincinnati

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Zelimir Djurcic Physics Department Columbia University

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  1. Status of MiniBooNE Experiment Zelimir Djurcic Physics Department Columbia University WIN07, Calcutta January 15-20,2007

  2. MiniBooNE Collaboration Y. Liu, D.Perevalov, I. Stancu Alabama S. Koutsoliotas Bucknell R.A. Johnson, J.L. Raaf Cincinnati T. Hart, R.H. Nelson, M.Tzanov, E.D. Zimmerman, M.Wilking Colorado A. Aguilar-Arevalo, L.Bugel, L. Coney, J.M. Conrad,Z. Djurcic, J. Monroe, K. Mahn, D. Schmitz, M.H. Shaevitz, M. Sorel, G.P. Zeller Columbia D. Smith Embry Riddle L.Bartoszek, C. Bhat, S J. Brice, B.C. Brown, D.A. Finley, R. Ford, F.G.Garcia, P. Kasper, T. Kobilarcik, I. Kourbanis, A. Malensek, W. Marsh, P. Martin, F. Mills, C. Moore, E. Prebys, A.D. Russell, P. Spentzouris, R. Stefanski, T. Williams Fermilab D. C. Cox, A. Green, T.Katori, H.-O. Meyer, C. Polly, R. Tayloe Indiana G.T. Garvey, C. Green, W.C. Louis, G.McGregor, S.McKenney, G.B. Mills, H. Ray, V. Sandberg, B. Sapp, R. Schirato, R. Van de Water, D.H. White Los Alamos R. Imlay, W. Metcalf, S. Ouedraogo, M. Sung, M.O. Wascko Louisiana State J. Cao, Y. Liu, B.P. Roe, H. Yang Michigan A.O. Bazarko, P.D. Meyers, R.B. Patterson, F.C. Shoemaker, H.A.Tanaka Princeton A. Currioni, B.T. Fleming Yale P. Nienaber St. Mary’s U. of Minnesota E. Hawker Western Illinois U. J.Link Virginia State U. MiniBooNE consists of about 70 scientists from 16 institutions.

  3. Before MiniBooNE Zelimir Djurcic-WIN2007

  4. Before MiniBooNE: The LSND Experiment LSND took data from 1993-98 - 49,000 Coulombs of protons - L = 30m and 20 < En< 53 MeV Saw an excess ofe:87.9 ± 22.4 ± 6.0 events. With an oscillation probability of (0.264 ± 0.067 ± 0.045)%. 3.8 s significance for excess. Oscillations? Signal: p e+ n n p  d (2.2MeV) Zelimir Djurcic-WIN2007

  5. Current Oscillation Status This signal looks very different from the others... • Much higher Dm2 = 0.1 – 10 eV2 • Much smaller mixing angle • Only one experiment! Kamioka, IMB, Super K, Soudan II, Macro, K2K Dm2 = 2.510-3 eV2 Homestake, Sage, Gallex, Super-K SNO, KamLAND Dm2 = 8.210-5 eV2 In SM there are only 3 neutrinos Zelimir Djurcic-WIN2007

  6. Confirming or Refuting LSND Fit to oscillation hypothesis Backgrounds • Want the same L/E • Want higher statistics • Want different systematics • Want different signal signature and backgrounds Need definitive study of e at high m2 … MiniBooNE Zelimir Djurcic-WIN2007

  7. MiniBooNE (Booster Neutrino Experiment) Zelimir Djurcic-WIN2007

  8. Search for e appearance in  beam Use protons from the 8 GeV booster Neutrino Beam <E>~ 1 GeV FNAL 8 GeV Beamline 50 m decay pipe MiniBooNE Detector: 12m diameter sphere 950000 liters of oil (CH2) 1280 inner PMTs 240 veto PMTs decay region:   ,  K   “little muon counters:” measure K flux in-situ magnetic horn: meson focusing  →e? absorber: stops undecayed mesons magnetic focusing horn Zelimir Djurcic-WIN2007 e ???

  9. Few words on: -Neutrino Flux -Cross-section -Detector Modeling Zelimir Djurcic-WIN2007

  10. Flux at MiniBooNE Detector • intrinsic ne • ~10-3 • m+ e+nmne • K+  p0 e+ne (also KL) p • nm • mainly fromp+ m+ nm • <En> ~ 700 MeV Flux simulation uses Geant4 Monte Carlo Meson production is based on Sanford-Wang parameterization of p-Be interaction cross-section. predicted flux • E910: , K production @ 6, 12, 18 GeV • w/thin Be target • HARP: , K production @ 8 GeV • w/ 5, 50, 100%  thick Be target

  11. Low Energy  Cross Sections • Predictions fromNUANCE • - MC which MiniBooNE uses • - open source code • - supported & maintained • by D. Casper (UC Irvine) • - standard inputs • - Smith-Moniz Fermi Gas • - Rein-Sehgal 1 • - Bodek-Yang DIS Imperative is to precisely predict signal & bkgd rates for future oscillation experiments We need data on nuclear targets! (most past data on H2, D2) MINOS, NuMI K2K, NOvA MiniBooNE, T2K Super-K atmospheric  Current cross-section studies devoted to understanding of the backgrounds in the MiniBooNE appearance signal.

  12. Neutrino Interactions in the Detector We are looking for e : nen  e-p • 48% QE • 31% CC + • 1% NC elastic • 8% NC 0 • 5% CC 0 • 4% NC +/- • 4% multi- Current Collected data: 700k neutrino candidates (before analysis cuts) for 7 x 1020 protons on target (p.o.t.) If LSND is correct, we expect several hundred e (after analysis cuts) from for e oscillations. NUANCE MC generator converts the flux into event rates in MiniBooNE detector Zelimir Djurcic-WIN2007

  13. Detector Modeling Detector (optical) model defines how light of generated event is propagated and detected in MiniBooNE detector Sources of light: Cerenkov (prompt, directional cone),and scintillation+fluorescence of oil (delayed, isotropic) Propagation of light: absorption, scattering (Rayleigh and Raman) and reflection at walls, PMT faces, etc. Strategy to verify model: External Measurements: emission, absorption of oil, PMT properties. Calibration samples: Laser flasks, Michel electrons, NC elastic events. Validation samples: Cosmic muons (tracker and cubes). Zelimir Djurcic-WIN2007

  14. Energy Calibration  e We have calibration sources spanning wide range of energies and all event types ! Michel electrons from  decay: provide E calibration at low energy (52.8 MeV), good monitor of light transmission, electron PID 12% E res at 52.8 MeV 0 mass peak: energy scale & resolution at medium energy (135 MeV), reconstruction cosmic ray  + tracker + cubes: energy scale & resolution at high energy (100-800 MeV), cross-checks track reconstruction PRELIMINARY provides  tracks of known length → E

  15. How to Detect and Reconstruct Neutrino Events Zelimir Djurcic-WIN2007

  16. Detector Operation Main trigger is an accelerator signal indicating a beam spill. Information is read out in 19.2 s interval covering arrival of beam and requests of various triggers (laser, random strobe, cosmic…). The rate of neutrino candidates was constant: 1.089 7 x 10-15 /P.O.T. Zelimir Djurcic-WIN2007

  17. Detector Operation and Event reconstruction Electronics continuously record charges and times of PMT hits. Backgrounds: cosmic muons and decay electrons No high level analysis needed to see neutrino events ->Simple cuts reduce non-beam backgrounds to ~10-3 To reconstruct an event: -Separate hits in beam window by time into sub-events of related hits -Reconstruction package maximizes likelihood of observed charge and time distribution of PMT hits to find track position, direction and energy (from the charge in the cone) for each sub-event Zelimir Djurcic-WIN2007

  18. Particle Identification Čerenkovrings provide primary means of identifying products of  interactions in the detector beam m candidate nmn m- p Michel e- candidate nen  e-p beam p0 candidate nmp nm pp0 n n p0→ gg Zelimir Djurcic-WIN2007

  19. Particle Identification II Angular distributions of PMT hits relative to track direction: muon PRELIMINARY Search for oscillation nen  e-p events is by detection of single electron like-rings, based on Čerenkovring profile. electron

  20. Signal Separation from Background Search for O(102) e oscillation events in O(105)  unoscillated events Backgrounds Reducible NC 0 (1 or 2 e-like rings) N decay (1 e-like ring) Single ring  events Irreducible Intrinsic e events in beam from K/ decay Signal p0→g g N

  21. Background Rejection and Blind Analysis Two complementary approaches for reducible background “Simple” cuts+Likelihood: easy to understand Boosted decision trees: maximize sensitivity MiniBooNE is performing a blind analysis: • We do not look into the data region where the oscillation candidates • are expected (“closed box”). • We are allowed to use: • Some of the info in all of the data • All of the info in some of the data • (But NOT all of the info in all of the data) Zelimir Djurcic-WIN2007

  22. Boosting PID Algorithm Boosted decision trees: • Go through all PID variables and find best • variable and value to split events. • For each of the two subsets repeat • the process • Proceeding in this way a tree is built. • Ending nodes are called leaves. • After the tree is built, additional trees • are built with the leaves re-weighted. • The process is repeated until best S/B • separation is achieved. • PID output is a sum of event scores from • all trees (score=1 for S leaf, -1 for B leaf). Reference NIM A 543 (2005) 577. Boosting Decision Tree Boosted Decision Trees at MiniBooNE: Use about 200 input variables to train the trees -target specific backgrounds -target all backgrounds generically PRELIMINARY Muons Electrons Zelimir Djurcic-WIN2007

  23. Likelihood Approach Compare observed light distribution to fit prediction: Does the track actually look like an electron? Apply likelihood fits to three hypotheses: -single electron track -single muon track -two electron-like rings (0 event hypothesis ) Form likelihood differences using minimized –logL quantities: log(Le/L) and log(Le/L) log(Le/L) log(Le/L)<0-like events log(Le/L)>0e-like events PRELIMINARY Zelimir Djurcic-WIN2007

  24. CCQE and 0Analysis Zelimir Djurcic-WIN2007

  25. MiniBooNE Quasi-Elastic Data  12C - beam   l CCQE events are used because one can use CCQE kinematics to reconstruct the neutrino energy– one can look at neutrino energy spectra We are looking for an oscillation signal in an EnQE distribution of electron events One can use an EnQEdistribution of muon events to understand our models nm n → m- p Cerenkov 1 e  12C nm Cerenkov 2 n Scintillation p Compare data to the Smith Moniz model implemented in NUANCE for nm CCQE events measure visible E and  from mostly Čerenkov () + some scintillation light (p) 90% purity sample Main bkgd: CC+ (+ absorbed)

  26. MiniBooNE Quasi-Elastic Data Deficit is seen in the data for low values of the momentum transfer, Q2 Similar effects have been seen in other channels and by other experiments Given the Fermi gas model approximation used one can imagine deficiencies – particularly in the low Q2 (very forward) kinematic region Use nm data sample to adjust available parameters in present model to reproduce data: only nm – ne differences are due to lepton mass effects, nmvs. ne With the high statistics and resolutions attainable at MiniBooNE, the MiniBooNE data will be used in the future to carefully study this and other models of CCQE interactions Zelimir Djurcic-WIN2007

  27. 0 ’s Background Determination if ’s highly asymmetric in energy or small opening angle (overlapping rings) can appear much like primary electron emerging from a e QE interaction! e appearance: 0 production important because background to →e Signal p0→g g N Reconstruction of π0 results in excellent Data/MC agreement. We use Data to reweight (i.e. tune) NUANCE rate prediction as a function of π0 momentum. PRELIMINARY We measure rate of π0 in the data sample out of the oscillation region and extrapolate it into the oscillation region.

  28. Data Un-smearing and efficiency correction The reconstructed γγ mass distribution is divided into 9 momentum bins. MC is used to unsmear the data: Monte Carlo Events Passing Analysis Cuts All events Events with no π0 • In bins of true momentum vs. reconstructed momentum, count MC events, over BG, in the signal window. • Divide by the total number of π0 events generated in that true momentum bin. • Invert the matrix. • Perform a BG subtraction on the data in each reconstructed momentum bins. • Multiply the data vector by the MC unsmearing Matrix

  29. The Corrected Data Distribution The corrected π0 momentum distribution is softer than the default Monte Carlo. The normalization discrepancy is across all interaction channels in MiniBooNE. From this distribution we derive a reweighting function for Monte Carlo events. MC: Generated distribution. Data: Corrected to true momentum and 100% efficiency. Ratio of data and MC points scaled to equal numbers of events. Zelimir Djurcic-WIN2007

  30. Reweighting MC to Data • The plots are: • Decay opening angle • Energy of high energy γ • Energy of low energy γ • π angle wrt the beam • The disagreement cos θπ may be due to coherent π0 production which we fit for. Reweighting improves data/MC agreement. Zelimir Djurcic-WIN2007

  31. The Resulting π0 MisID Distribution The resulting misID distribution is softer in Eν QE. Also there are less misID events per produced π0 than in the default Monte Carlo. The error on misID yield is well below the 10% target. This is not the final PID cut set! PRELIMINARY Zelimir Djurcic-WIN2007

  32. Cross-Checks Zelimir Djurcic-WIN2007

  33. Important Cross-check… … comes from NuMI events detected in MiniBooNE detector! We get e,  , 0 , +/- , ,etc. events from NuMI in MiniBooNE detector, all mixed together Use them to check our e reconstruction and PID separation! Remember that MiniBooNE conducts a blind data analysis! We do not look in MiniBooNE data region where the osc. e are expected… The beam at MiniBooNE from NuMI is significantly enhanced in e from K decay because of the off-axis position. MiniBooNE  Decay Pipe Beam Absorber NuMI events cover whole energy region relevant to e osc. analysis at MiniBooNE.

  34. Example of use of the events from NuMI beam Boosted Decision Tree Likelihood Ratios e/ PRELIMINARY PRELIMINARY PRELIMINARY e/ Data/MC agree through background and signal regions

  35. Appearance Signal and Backgrounds Zelimir Djurcic-WIN2007

  36. Appearance Signal and Backgrounds • Oscillation e • Example (fake) • oscillation signal • m2 = 1 eV2 • sin22 = 0.004 • Fit for excess as • function of • reconstructed e • energy PRELIMINARY Arbitrary Units Zelimir Djurcic-WIN2007

  37. Appearance Signal and Backgrounds • MisID  • of these…… • ~83% 0 • Only ~1% of 0s are misIDed • Determined by clean 0 measurement • ~7%  decay • Use clean 0 measurement to estimate  production • ~10% other • Use  CCQE rate to normalize and MC for shape PRELIMINARY Arbitrary Units Zelimir Djurcic-WIN2007

  38. Appearance Signal and Backgrounds nm p+Be p+ ne m+ nme+ • e from + • Measured with  CCQE sample • Same parent + kinematics • Most important low E background • Very highly constrained (a few percent) PRELIMINARY Arbitrary Units Zelimir Djurcic-WIN2007

  39. Appearance Signal and Backgrounds • e from K+ • Use High energy e and  to normalize • Use kaon production data for shape PRELIMINARY Arbitrary Units Zelimir Djurcic-WIN2007

  40. Appearance Signal and Backgrounds • High energy e • data • Events below 1.5 GeV still in closed box (blind analysis) PRELIMINARY Arbitrary Units Zelimir Djurcic-WIN2007

  41. Combined Fit (Example) Scan sin2(2)e,Dm2,with sin2(2)x=0; calculate 2 value over nm andne bins: 2=I,J(OI-PI)(CIJ)-1(OJ-PJ) PI =PI (sin2(2),Dm2) Systematic error matrix CIJ includes estimated systematic uncertainties PRELIMINARY   nmne nm CIJ = nmne ne Combined fit constrains uncertainties common to nmand ne Zelimir Djurcic-WIN2007

  42. Evaluating Systematics Reconstructed visible muon energy (left) muon neutrino energy (right) using CCQE data. Error bands show both statistical and systematic errors PRELIMINARY Zelimir Djurcic-WIN2007

  43. MiniBooNE Oscillation Sensitivity MiniBooNE aims to cover LSND region. We are currently finalizing work on systematic error (i.e. error matrix) that combines the error sources (flux,  or measured rate, detector modeling)of signal and the background components to predict sensitivity to oscillation signal  LSND best fit sin22 = 0.003 m2 = 1.2 eV2 Zelimir Djurcic-WIN2007

  44. Summary Total accumulated dataset 7.5 x 1020 POT, world’s largest dataset in this energy range. Jan 2006: Started running with antineutrinos. Detected NuMI neutrinos – using in analysis. Oscillation Analysis progress: we are preparing to open the closed “oscillation box”. Zelimir Djurcic-WIN2007

  45. Backup Slides Zelimir Djurcic-WIN2007

  46. Explaining the LSND result • Sterile Neutrinos • RH neutrinos that don’t interact (Weak == LH only) • CPT Violation • 3 neutrino model, manti-2 > m2 • Run in neutrino, anti-neutrino mode, compare measured oscillation probability • Mass Varying Neutrinos • Mass of neutrinos depends on medium through which it travels • Lorentz Violation • Oscillations depend on direction of propagation • Oscillations explained by small Lorentz violation • Don’t need to introduce neutrino mass for oscillations! • Look for sidereal variations in oscillation probability Zelimir Djurcic-WIN2007

  47. World p+Be Measurements • E910: , K production @ 6, 12, 18 GeV w/thin Be target • HARP: , K production @ 8 GeV w/ 5, 50, 100%  thick Be target Zelimir Djurcic-WIN2007

  48. HARP Results HARP (CERN) Data taken with MiniBooNE target slugs using 8 GeV beam Results on thin target just added (Apr06). • black boxes are the distribution of + which decay to a  that passes through the MiniBooNE detector kinematic boundary of HARP measurement at exactly 8.9 GeV/c Further improvement in flux prediction expected soon with HARP thick target and K data

  49. About NUANCE Exclusive channels are handled separately and use differing, appropriate models Total cross-sections are then the sum of all relevant exclusive channels Nuclear effects of hadrons propagating through the nucleus are considered to give you an expected final state condition The most critical exclusive channel for the MiniBooNE oscillation search is the charged-current quasi-elastic interaction NUANCE models CCQE events using the relativistic Fermi gas model of Smith and Moniz as a framework The next most critical exclusive channels are the NC production of NC 0's NUANCE uses the resonant and coherent p0 production models of Rein and Sehgal Zelimir Djurcic-WIN2007

  50. NuMI events at MiniBooNE MiniBooNE q p, K p beam Decay Pipe • NuMI n’s sprayed in all directions. • Kmn and pmn decays at off-axis angle: ~110mrad to MiniBooNE • Opportunity to check the p/K ratio of yields off the target. Zelimir Djurcic-WIN2007

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