Neutrino Oscillation Search at MiniBooNE

1. Neutrino Oscillation Search at MiniBooNE Zelimir Djurcic Physics Department Columbia University Neutrino Oscillation Workshop Conca Specchiulla, Italy September 9-16, 2006 Zelimir Djurcic - NOW2006

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 - NOW2006

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 - NOW2006

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 - NOW2006

6. 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 - NOW2006

7. 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 - NOW2006

8. MiniBooNE (Booster Neutrino Experiment) Zelimir Djurcic - NOW2006

9. 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 - NOW2006 e ???

10. Few words on: -Neutrino Flux -Cross-section -Detector Modeling Zelimir Djurcic - NOW2006

11. 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. Model includes target, horn, decay pipe, and surrounding materials (re-interaction, decays) Zelimir Djurcic - NOW2006 predicted flux

12. 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 - NOW2006

13. HARP Results HARP (CERN) Data taken with MiniBooNE target slugs using 8 GeV beam Results on thin target just added (Apr06). See G.Catanesi’s Talk! Further improvement in flux prediction expected soon with HARP thick target and K data Zelimir Djurcic - NOW2006

14. 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 NUANCE MC generator converts the flux into event rates in MiniBooNE detector MiniBooNE Zelimir Djurcic - NOW2006

15. 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. Zelimir Djurcic - NOW2006

16. Detector Modeling Detector (optical) model defines how light of generated event is propagated and detected in MiniBooNE detector Sources of light: mineral oil more complicated than pure water (Cerenkov only) or liquid scintillator (Cerenkov negligible) detector.We have both Cerenkov radiation (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). Goal is good agreement between data/MCfor all variables used in event classification to allow level of separation needed for e appearance search (therefore: syst.err. At least as flux errors, for example)

17. External Measurements Performed variety of stand alone tests which characterize separate components of mineral oil Zelimir Djurcic - NOW2006

18. Internal Calibration Sources Laser flasks (4) : used to measure tube charge, timing response Corrected time = PMT time – TOF – event time(e.e. laser pulse) Neutral Current Elastic sample : provides neutrino sample, protons below Cerenkov threshold == isolate scintillation components, distinguish from fluorescence of detector Zelimir Djurcic - NOW2006

19. 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

20. Optical Model Chain External Measurements and Laser Calibration First Calibration with Michel Data Calibration of Scintillation Light with NC Events Final Calibration with Michel Data Validation with Cosmic Muons, CCQE, e NuMI, etc. Zelimir Djurcic - NOW2006

21. Recent Improvements Energy calibration: Ratio of Michel electron Energy (Monte Carlo to Data) as a function of position and direction Improvements to OM greatly improve Michel electron energy as a function of location in our detector Zelimir Djurcic - NOW2006

22. How to Detect and Reconstruct Neutrino Events Zelimir Djurcic - NOW2006

23. Detector Operation and Event reconstruction Electronics continuously record charges and times of PMT hits. Information is read out in 19.2 s interval covering arrival of beam and requests of various triggers (laser, random strobe, cosmic…). No high level analysis needed to see neutrino events Backgrounds: cosmic muons and decay electrons ->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 -Reconstruct main track of each sub-event. Reconstruction package maximizes likelihood of observed charge and time distribution of PMT hits to find track position, direction (from Cerenkov cone) and energy (from the charge in the cone) -Perform particle identification on primary track(s).

24. 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 - NOW2006

25. Early Late Low High Charge (Size) Time (Color) Zelimir Djurcic - NOW2006 First the muon enters the tank and stops...

26. Early Late Low High Charge (Size) Time (Color) Zelimir Djurcic - NOW2006 First the muon enters the tank and stops...

27. ...Then the Michel electron is observed Michel Energy Distribution Muons provide high energy calibration Michels provide low energy calibration Zelimir Djurcic - NOW2006

28. 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

29. 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

30. 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 - NOW2006

31. 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

32. 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

33. 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

34. Appearance Signal and Backgrounds Zelimir Djurcic - NOW2006

35. Appearance Signal and Backgrounds Osc e MisID  e from + e from K+ e from K0 e from + Oscillation e Example oscillation signal • m2 = 1 eV2 • sin22 = 0.004 Fit for excess as function of reconstructed e energy Arbitrary Units Zelimir Djurcic - NOW2006

36. 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 Arbitrary Units Zelimir Djurcic - NOW2006

37. 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) Arbitrary Units Zelimir Djurcic - NOW2006

38. 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 Arbitrary Units Zelimir Djurcic - NOW2006

39. 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) Arbitrary Units Zelimir Djurcic - NOW2006

40. Appearance Signal and Backgrounds Signal predicted on LSND very small (about 0.25% oscillation probability) Several background components of the size comparable to expected signal Our approach is to measure each of the background components from our own data: do not rely on Monte Carlo. Zelimir Djurcic - NOW2006

41. 0 Background Determination Our main objective is to measure the rate of π0 misidentification in the νe oscillation sample and to determine the misID energy spectrum. 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. Zelimir Djurcic - NOW2006

42. 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

43. 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 - NOW2006

44. 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 - NOW2006

45. Coherent Fit Effect Fit coherent, resonant, and background components to the data Reweighted 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. Unweighted Zelimir Djurcic - NOW2006

46. 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 - NOW2006

47. Cross-Checks Zelimir Djurcic - NOW2006

48. 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.

49. Events from NuMI beam Boosted Decision Tree Likelihood Ratios e/ PRELIMINARY PRELIMINARY e/ Data/MC agree through background and signal regions Zelimir Djurcic - NOW2006

50. Where are we? Zelimir Djurcic - NOW2006