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Zelimir Djurcic Columbia University

Search for Oscillation Signal at MiniBooNE. Zelimir Djurcic Columbia University. 6 th Rencontres du Vietnam, Hanoi, Vietnam, 2006. Before MiniBooNE: The LSND Experiment. LSND took data from 1993-98 - 49,000 Coulombs of protons - L = 30m and 20 < E n < 53 MeV.

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Zelimir Djurcic Columbia University

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  1. Search for Oscillation Signal at MiniBooNE Zelimir Djurcic Columbia University 6th Rencontres du Vietnam, Hanoi, Vietnam, 2006

  2. 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) Need definitive study of e at high m2 … MiniBooNE

  3. MiniBooNE (Booster Neutrino Experiment) Zelimir Djurcic - Hanoi 2006

  4. 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 e ???

  5. 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

  6. 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 - Hanoi 2006

  7. 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

  8. 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

  9. 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 - Hanoi 2006

  10. 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

  11. 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

  12. log(Le/L):Current 0 Studies • Ntank > 200, Nveto < 6, Fid.Vol. • No Michel electron • 2-ring fit on all events Reconstructed 0 mass Translate reconstructed0 events into the spectrum of mis-identified events! PRELIMINARY Not looked into this region: expect osc. candidates (blindness) The data is used to test likelihood based e/0 separation. PRELIMINARY Good data/MC agreement demonstrates robust 0 reconstruction

  13. Appearance Signal and Backgrounds Osc e MisID  e from + e from K+ e from K0 e from + Full data sample ~5.3 x 1020 POT Oscillation e Example oscillation signal • m2 = 1 eV2 • sin22 = 0.004 Fit for excess as function of reconstructed e energy Zelimir Djurcic - Hanoi 2006

  14. Appearance Signal and Backgrounds Osc ne MisID nm ne from m+ ne from K+ ne from K0 ne from p+ 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 Zelimir Djurcic - Hanoi 2006

  15. Appearance Signal and Backgrounds Osc ne nm p+Be p+ ne m+ nme+ MisID nm ne from m+ ne from K+ ne from K0 ne from p+ e from + • Measured with  CCQE sample • Same parent + kinematics • Most important low E background • Very highly constrained (a few percent) Zelimir Djurcic - Hanoi 2006

  16. Appearance Signal and Backgrounds Osc ne MisID nm ne from m+ ne from K+ ne from K0 ne from p+ e from K+ • Use High energy e and  to normalize • Use kaon production data for shape Zelimir Djurcic - Hanoi 2006

  17. Appearance Signal and Backgrounds Osc ne MisID nm ne from m+ ne from K+ ne from K0 ne from p+ High energy e data • Events below ~2.0 GeV still in closed box (blind analysis) Zelimir Djurcic - Hanoi 2006

  18. 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.

  19. Events from NuMI beam Boosted Decision Tree Likelihood Ratios e/ PRELIMINARY PRELIMINARY e/ Data/MC agree through background and signal regions

  20. MiniBooNE Oscillation Sensitivity MiniBooNE aims to cover LSND region. Almost there, with final work on systematic error determination  LSND best fit sin22 = 0.003 m2 = 1.2 eV2 Zelimir Djurcic - Hanoi 2006

  21. Recent MiniBooNE Progress 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: results are expected soon. Zelimir Djurcic - Hanoi 2006

  22. Backup Slides Zelimir Djurcic - Hanoi 2006

  23. More0 Studies Zelimir Djurcic - Hanoi 2006

  24. MiniBooNE CC+ Cross-Section Obtained by multiplying measuredCC +/QEratio by QE  prediction(QE with MA=1.03 GeV, BBA non-dipole vector form factors) Efficiency corrected CC +/QE  Ratio measuremet on CH2 current systematics estimate: - light propagation in oil: ~20% -  cross sections: ~15% - energy scale: ~10% - statistics: ~5% ~25% lower than prediction, but within errors

  25. PID Inputs Calibration Sample Signal-like Events Primary Background Mean = 1.80, RMS = 1.47 Mean = 1.19, RMS = 0.76 Mean = 20.83, RMS = 25.59 Mean = 3.48, RMS = 3.17 Mean = 16.02, RMS = 25.90 Mean = 3.24, RMS = 2.94

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