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

MiniBooNE consists of about 70 scientists from 16 institutions. 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. MiniBooNECollaboration

Zelimir Djurcic-WIN2007 Before MiniBooNE

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

Zelimir Djurcic-WIN2007 Kamioka, IMB, Super K, Soudan II, Macro, K2K  m 2 = 2.5  eV 2 Homestake, Sage, Gallex, Super-K SNO, KamLAND  m 2 = 8.2  eV 2 This signal looks very different from the others... Much higher  m 2 = 0.1 – 10 eV 2 Much smaller mixing angle Only one experiment! Current Oscillation Status In SM there are only 3 neutrinos

Zelimir Djurcic-WIN2007 Want the same L/E Want higher statistics Want different systematics Want different signal signature and backgrounds Fit to oscillation hypothesis Backgrounds Confirming or Refuting LSND Need definitive study of   e at high  m 2 … MiniBooNE

Zelimir Djurcic-WIN2007 MiniBooNE (Booster Neutrino Experiment)

Zelimir Djurcic-WIN2007 magnetic horn: meson focusing decay region:    , K    absorber: stops undecayed mesons “little muon counters:” measure K flux in-situ  → e ? 50 m decay pipe magnetic focusing horn FNAL 8 GeV Beamline Search for e appearance in  beam   e ???   e ??? Use protons from the 8 GeV booster  Neutrino Beam ~ 1 GeV MiniBooNE Detector: 12m diameter sphere liters of oil liters of oil (CH 2 ) 1280 inner PMTs 240 veto PMTs

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

  mainly from        ~ 700 MeV predicted flux  intrinsic e ~10 -3  +  e +  e K +   0 e + e (also K L )  Flux at MiniBooNE Detector Flux at MiniBooNE Detector Flux simulation uses Geant4 Monte Carlo Meson production is based on Sanford- Wang parameterization of p-Be interaction cross-section. p E910: , K 6, 12, 18 GeV w/thin Be target HARP: , K 8 GeV w/ 5, 50, 100% thick Be target

MINOS, NuMI K2K, NOvA MiniBooNE, T2K Super-K atmospheric Predictions from NUANCE - 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 Low Energy Cross Sections Imperative is to precisely predict signal & bkgd rates for future oscillation experiments We need data on nuclear targets! (most past data on H 2, D 2 ) Current cross-section studies devoted to understanding of the backgrounds in the MiniBooNE appearance signal.

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

Zelimir Djurcic-WIN2007 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).

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

Zelimir Djurcic-WIN2007 How to Detect and Reconstruct Neutrino Events

Zelimir Djurcic-WIN2007 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…). Detector Operation The rate of neutrino candidates was constant: x /P.O.T.

Zelimir Djurcic-WIN2007 Detector Operation and Event reconstruction 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 No high level analysis needed to see neutrino events Backgrounds: cosmic muons and decay electrons ->Simple cuts reduce non-beam backgrounds to ~10 -3 Electronics continuously record charges and times of PMT hits.

Zelimir Djurcic-WIN2007  0 →  Michel e - candidate beam  candidate beam  0 candidate Čerenkov rings provide primary means of identifying products of interactions in the detector  n   - p e n  e - p  p   p  0 n Particle Identification

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

Signal Separation from Background 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  0 →  Signal  N  Search for O( 10 2 ) e oscillation events in O( 10 5 )  unoscillated events Backgrounds

Zelimir Djurcic-WIN2007 Two complementary approaches for reducible background “Simple” cuts+Likelihood: easy to understand Boosted decision trees: maximize sensitivity Background Rejection and Blind Analysis 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 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). Boosting PID Algorithm Boosted Decision Trees at MiniBooNE: Use about 200 input variables to train the trees -target specific backgrounds -target all backgrounds generically Boosting Decision Tree Muons Electrons PRELIMINARY Reference NIM A 543 (2005) 577.

Zelimir Djurcic-WIN2007 Likelihood Approach 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(L e /L  ) and log(L e /L  ) Compare observed light distribution to fit prediction: Does the track actually look like an electron? log(L e /L  ) log(L e /L  )<0  -like events log(L e /L  )>0 e-like events PRELIMINARY

Zelimir Djurcic-WIN2007 CCQE and  0 Analysis

MiniBooNE Quasi-Elastic Data  12 C - 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 E QE distribution of electron events One can use an E QE distribution of muon events to understand our models measure visible E and  from mostly Čerenkov (  ) + some scintillation light (p) 90% purity sample Main bkgd: CC  + (  + absorbed) p  n Scintillation Cerenkov 1 12 C Cerenkov 2 e  Compare data to the Smith Moniz model implemented in NUANCE for  CCQE events  n →  - p

Zelimir Djurcic-WIN2007 Deficit is seen in the data for low values of the momentum transfer, Q 2 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 Q 2 (very forward) kinematic region Use  data sample to adjust available parameters in present model to reproduce data: only  – e differences are due to lepton mass effects,  vs. e 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 MiniBooNE Quasi-Elastic Data

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

The reconstructed γγ mass distribution is divided into 9 momentum bins. MC is used to unsmear the data: 1.In bins of true momentum vs. reconstructed momentum, count MC events, over BG, in the signal window. 2.Divide by the total number of π 0 events generated in that true momentum bin. 3.Invert the matrix. 4.Perform a BG subtraction on the data in each reconstructed momentum bins. 5.Multiply the data vector by the MC unsmearing Matrix Monte Carlo Events Passing Analysis Cuts All events Events with no π 0 Data Un-smearing and efficiency correction

Zelimir Djurcic-WIN2007 The Corrected Data Distribution The corrected π 0 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. Ratio of data and MC points scaled to equal numbers of events. MC: Generated distribution. Data: Corrected to true momentum and momentum and 100% efficiency. 100% efficiency.

Zelimir Djurcic-WIN2007 Reweighting improves data/MC agreement. The plots are: Decay opening angle Decay opening angle Energy of high energy γ Energy of high energy γ Energy of low energy γ Energy of low energy γ π angle wrt the beam π angle wrt the beam The disagreement cos θ π may be due to coherent π 0 production which we fit for. Reweighting MC to Data

Zelimir Djurcic-WIN2007 The Resulting π 0 MisID Distribution The resulting misID distribution is softer in E ν QE. Also there are less misID events per produced π 0 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 Cross-Checks

Important Cross-check… … comes from NuMI events detected in MiniBooNE detector! MiniBooNE Decay Pipe Beam Absorber We get e, ,  0,  +/-, ,etc. events from NuMI in MiniBooNE detector, all Use them to check 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. NuMI events cover whole energy region relevant to e osc. analysis at MiniBooNE.

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

Zelimir Djurcic-WIN2007 Appearance Signal and Backgrounds

Zelimir Djurcic-WIN2007 Arbitrary Units Oscillation e Example (fake) oscillation signal –  m 2 = 1 eV 2 –sin 2 2  = Fit for excess as function of reconstructed e energy Appearance Signal and Backgrounds PRELIMINARY

Zelimir Djurcic-WIN2007 Arbitrary Units Appearance Signal and Backgrounds MisID  of these…… ~83%  0 –Only ~1% of  0 s 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

Zelimir Djurcic-WIN2007 Arbitrary Units Appearance Signal and Backgrounds e from  + Measured with  CCQE sample –Same parent  + kinematics Most important low E background Very highly constrained (a few percent) PRELIMINARY   p+Be  + e  +   e +

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

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

Zelimir Djurcic-WIN2007 Combined Fit (Example) Combined fit constrains uncertainties common to Combined fit constrains uncertainties common to  and e =  2 =  I,J (O I -P I )(C IJ ) -1 (O J -P J ) Systematic error matrix includes estimated C IJ includes estimated systematic uncertainties   C IJ =   e e PRELIMINARY P I =P I ( sin 2 (2  ),  m 2 ) Scan Scan sin 2 (2  )  e,  m 2,with sin 2 (2  )  x =0; calculate  2 value over  and  ebins:

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

Zelimir Djurcic-WIN2007 LSND best fit sin 2 2  =  m 2 = 1.2 eV 2 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

Zelimir Djurcic-WIN2007 Total accumulated dataset 7.5 x 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”. Summary

Zelimir Djurcic-WIN2007 Backup Slides

Zelimir Djurcic-WIN2007 –Sterile Neutrinos RH neutrinos that don’t interact (Weak == LH only) –CPT Violation 3 neutrino model,  m anti- 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 Explaining the LSND result

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

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

Zelimir Djurcic-WIN2007 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 p 0 production models of Rein and Sehgal About NUANCE

Zelimir Djurcic-WIN2007 NuMI ’s sprayed in all directions. K  and  decays at off-axis angle: p beam , K Opportunity to check the  /K ratio of yields off the target.  ~110mrad to MiniBooNE NuMI events at MiniBooNE

Zelimir Djurcic-WIN2007 Production of the  0 ’s Resonant  0 production  N     N=(p,n)    0 N’ Coherent  0 production  A   A  0  0 →  In addition to its primary decay  N, the  resonance has a branching fraction of 0.56% to N  final state. e appearance:  0 production important because background to  → e if  ’s highly asymmetric in energy or small opening angle (overlapping rings) can appear much like primary electron emerging from a e QE interaction!  0 →  Signal  N

Zelimir Djurcic-WIN2007 Reweighted Unweighted The fit coherent fraction is higher after reweighting. This was expected based on the additional peaking in the reweighted cos θ distribution. The reweighted fit does much better in the important forward region. Fit coherent, resonant, and background components to the data Coherent Fit Effect