Zelimir Djurcic Physics Department

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Neutrino Oscillation Search at MiniBooNE
Zelimir Djurcic Physics Department Columbia
University
Neutrino Oscillation Workshop Conca Specchiulla,
Italy September 9-16, 2006
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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
MiniBooNE Collaboration
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. Marys U. of Minnesota E. Hawker
Western Illinois U. J.Link Virginia State U.
MiniBooNE consists of about 70 scientists from 16
institutions.
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Before MiniBooNE
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Before MiniBooNE The LSND Experiment
LSND took data from 1993-98 - 49,000 Coulombs
of protons - L 30m and 20 lt Enlt 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)
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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

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Explaining the LSND result

• Sterile Neutrinos
• RH neutrinos that dont interact (Weak LH
  only)
• CPT Violation
• 3 neutrino model, ?manti-?2 gt ?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
• Dont need to introduce neutrino mass for
  oscillations!
• Look for sidereal variations in oscillation
  probability

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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
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MiniBooNE
(Booster Neutrino Experiment)
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Search for ?e appearance in ?? beam
Use protons from the 8 GeV booster ? Neutrino
Beam ltE?gt 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 ???
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Few words on -Neutrino Flux -Cross-section -Detec
tor Modeling
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? Flux at MiniBooNE Detector

• nm
• mainly from p ? m nm
• ltEngt 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)
predicted flux
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World pBe Measurements

• E910 ?, K production _at_ 6, 12, 18 GeV w/thin Be
  target
• HARP ?, K production _at_ 8 GeV w/ 5, 50, 100 ?
  thick Be target

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HARP Results
HARP (CERN) Data taken with MiniBooNE target
slugs using 8 GeV beam Results on thin
target just added (Apr06).
See G.Catanesis Talk!
Further improvement in flux prediction expected
soon with HARP thick target and K data
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Low Energy ? Cross Sections
NUANCE MC generator converts the flux into event
rates in MiniBooNE detector
MiniBooNE
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Neutrino Interactions in the Detector
ne n ? e- p
We are looking for ????e

• 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.
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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 scintillationfluorescence 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/MC for 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)
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External Measurements
Performed variety of stand alone tests which
characterize separate components of mineral oil
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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
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Energy Calibration
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?
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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.
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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
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How to Detect and Reconstruct Neutrino Events
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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
-gtSimple 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).
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Particle Identification
Cerenkov rings provide primary means of
identifying products of ? interactions in
the detector
beam m candidate
nm n ? m- p
Michel e- candidate
ne n ? e- p
beam p0 candidate
nm p ? nm p p0
n n
p0 ? gg
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Charge (Size)
Time (Color)
First the muon enters the tank and stops...
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Charge (Size)
Time (Color)
First the muon enters the tank and stops...
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...Then the Michel electron is observed
Michel Energy Distribution

• Muons provide high energy calibration
• Michels provide low energy calibration

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Particle Identification II
Angular distributions of PMT hits relative to
track direction
muon
PRELIMINARY
Search for oscillation ne n ? e- p events is by
detection of single electron like-rings, based on
Cerenkov ring profile.
electron
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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
p0?g g
Signal
??N?
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Background Rejection and Blind Analysis
Two complementary approaches for reducible
background
Simple cutsLikelihood 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)

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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 (score1 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
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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?)lt0 ?-like events
log(Le/L?)gt0 e-like events
PRELIMINARY
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log(Le/L?) Current ?0 Studies

• Ntank gt 200, Nveto lt 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
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Appearance Signal and Backgrounds
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Appearance Signal and Backgrounds

• Oscillation ?e
• Example oscillation
• signal
• ?m2 1 eV2
• sin22? 0.004
• Fit for excess as
• function of
• reconstructed ?e
• energy

Arbitrary Units
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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

Arbitrary Units
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Appearance Signal and Backgrounds

• ?e from ?
• Measured with ?? CCQE sample
• Same parent ? kinematics
• Most important low E background
• Very highly constrained (a few percent)

Arbitrary Units
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Appearance Signal and Backgrounds

• ?e from K
• Use High energy ?e and ?? to normalize
• Use kaon production data for shape

Arbitrary Units
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Appearance Signal and Backgrounds

• High energy ?e
• data
• Events below 2.0 GeV still in closed box (blind
  analysis)

Arbitrary Units
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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.
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?0 Background Determination
Our main objective is to measure the rate of p0
misidentification in the ?e oscillation sample
and to determine the misID energy spectrum.
Reconstruction of p0 results in excellent
Data/MC agreement. We use Data to reweight (i.e.
tune) NUANCE rate prediction as a function of p0
momentum.
PRELIMINARY
We measure rate of p0 in the data sample out of
the oscillation region and extrapolate it into
the oscillation region.
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Data Un-smearing and efficiency correction
The reconstructed ?? 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 p0

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 p0 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

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The Corrected Data Distribution
The corrected p0 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.
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Reweighting MC to Data

• The plots are
• Decay opening angle
• Energy of high energy ?
• Energy of low energy ?
• p angle wrt the beam
• The disagreement cos ?p may be due to coherent p0
  production which we fit for.

Reweighting improves data/MC agreement.
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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 ?
distribution. The reweighted fit does much better
in the important forward region.
Unweighted
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The Resulting p0 MisID Distribution
The resulting misID distribution is softer in E?
QE. Also there are less misID events per
produced p0 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
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Cross-Checks
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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.
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Events from NuMI beam
Boosted Decision Tree
Likelihood Ratios
e/?
PRELIMINARY
PRELIMINARY
e/?
Data/MC agree through background and signal
regions
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Where are we?
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MiniBooNE Oscillation Sensitivity
MiniBooNE aims to cover LSND region. We are
currently optimizing PID cuts, and 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
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Last Slide
Total accumulated dataset 7.5 x 1020 POT,
worlds 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.
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Backup Slides
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MiniBooNE CC? Cross-Section
Obtained by multiplying measured CC ?/QE ratio
by QE ? prediction (?QE with MA1.03 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
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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