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4/19/20074/19/2007Jonathan LinkJonathan Link
First Oscillation Results from the First Oscillation Results from the MiniBooNE ExperimentMiniBooNE Experiment
Jonathan LinkJonathan Link
Virginia Polytechnic Institute & State UniversityVirginia Polytechnic Institute & State University
April 19April 19thth 2007 2007
4/19/20074/19/2007Jonathan LinkJonathan Link
University of Alabama Los Alamos National LaboratoryBucknell University Louisiana State UniversityUniversity of Cincinnati University of MichiganUniversity of Colorado Princeton UniversityColumbia University Saint Mary’s University of MinnesotaEmbry Riddle University Virginia Polytechnic InstituteFermi National Accelerator Laboratory Western Illinois UniversityIndiana University Yale University
The MiniBooNE CollaborationThe MiniBooNE Collaboration
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The LSND ExperimentThe LSND Experiment
LSND took data from 1993-98
Baseline of 30 meters
Energy range of 20 to 55 MeV
L/E of about 1m/MeV
LSND’s Signature
2.2 MeV neutron capture Čerenkov
Scintillation
π+ μ+ νμ
e+ νμνeνe p e+ n
n+H→D+γ
Neutrino oscillation probability:
P(νμ → ν
e) = sin22θ sin2(1.27Δm2L/E)
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The LSND SignalThe LSND SignalLSND found an excess of νe events in a νμ beam
They found 87.9 ± 22.4 ± 6.0 events over expectation.
With an oscillation probability of (0.264 ± 0.067 ± 0.045)%.
Decay in flight analysis (e) oscillationprobability of (0.10 ± 0.16 ± 0.04) %
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Why is this Result Interesting?Why is this Result Interesting?
LEP found that there are only 3 light neutrinos that interact weakly
ν3
ν2ν1
m22
m12
|m32| = m1
2 ± m22
Three neutrinos allow only 2 independent m2 scales
But there are experimental results at 3 different m2 scales
Existing Neutrino Oscillation Data
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What Does it Mean?What Does it Mean?First, one or more of the experiments may be wrong
LSND, being the leading candidate, has to be checked → MiniBooNE
Otherwise, add one or more sterile neutrinos…Giving you more independent m2 scales
Best fits to the data require at least two sterile
The Usual 3ν Model
(mas
s)2
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Keep L/E the same while changing systematics, neutrino energy and event signature
P(νμ → ν
e) = sin22θ sin2(1.27Δm2L/E)
Booster
K+
target and horn detectordirt decay region absorber
primary beam tertiary beamsecondary beam
(protons) (mesons) (neutrinos)
e
Order of magnitudehigher energy (~500 MeV)
than LSND (~30 MeV)
Order of magnitudelonger baseline (~500 m)
than LSND (~30 m)
The MiniBooNE Design StrategyThe MiniBooNE Design Strategy
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The Neutrino BeamThe Neutrino Beam
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Booster TargetHall
MiniBooNE extracts beam from the 8 GeV Booster…
delivers it to a 1.7 Be target…
within a magnetic horn(2.5 kV, 174 kA) thatincreases the flux by 6
These results correspond to (5.580.12) 1020 POT
The Neutrino BeamThe Neutrino Beam
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HARP (CERN) 5% λ Beryllium target 8.9 GeV proton beam momentum
Modeling Production of PionsModeling Production of Pions
HARP collaboration,hep-ex/0702024
Data are fit to a Sanford-Wangparameterization.
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K+ Data from 10 - 24 GeV.Uses a Feynman ScalingParameterization.
data ─ pointsdash ─ total error (fit parameterization)
Modeling Production of KaonsModeling Production of Kaons
K0 data are also parameterized.
In situ measurementof K+ from LMCagrees within errorswith parameterization
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μ+→ e+ νμνe
K→ πeνe
K+→ μ+νμ
π+→ μ+νμ
Antineutrino content: 6%
Neutrino Flux from GEANT4 SimulationNeutrino Flux from GEANT4 Simulation
“Intrinsic” νe + νe sources:
μ+ → e+ νμνe (52%)
K+ → π0 e+ νe (29%)K0 → π e νe (14%)
Other ( 5%)
νe/ νμ = 0.5%
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Stability of RunningStability of Running
Observed andexpected eventsper minute
Full ν Run Neutrino interactionsper proton-on-target.
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Events in the DetectorEvents in the Detector
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The MiniBooNE DetectorThe MiniBooNE Detector 541 meters downstream of target
12 meter diameter sphere
Filled with 950,000 liters of pure mineral oil — 20+ meter attenuation length
Light tight inner region with 1280 photomultiplier tubes
Outer veto region with 240 PMTs.
Simulated with a GEANT3 Monte Carlo
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Detected photons from• Prompt light (Cherenkov)• Late light (scintillation, fluorescence)
in a 3:1 ratio for ~1
Attenuation length: >20 m @ 400 nmWe have developed
39-parameter“Optical Model”
based on internal calibrationand external measurement
Optical and PMT Response ModelsOptical and PMT Response Models
Data/MC overlay
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The 1.6 μs spill sits inside a 19.2 μs beam trigger window
An unbroken time cluster of hits forms a “subevent”
Most events are from νμ charged current (CC) interactions with a characteristic two “subevent” structure from stopped μ decay
μ
e
Ta
nk
Hit
s
ExampleEvent
The Beam WindowThe Beam Window
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All events in beam window
Veto Hits <6 removes through-going cosmic rays
This leaves “Michel electrons” (ee) and beam events
Tank Hits > 200(effectively energy)removes Michel electrons, which havea 52 MeV endpoint
Progressively introducing cuts on the beam window:
Event Timing Structure with Simple Clean-up CutsEvent Timing Structure with Simple Clean-up Cuts
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Calibration Systems and SourcesCalibration Systems and Sources
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From the NUANCE event generator D. Casper, NPS, 112 (2002) 161
Event neutrino energy (GeV)
Predicted Event Rates Before CutsPredicted Event Rates Before Cuts
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CCQE are 39% of all events
Events are “clean” (few particles)Energy of the neutrino
can be reconstructed
Reconstructed from: Scattering angle Visible energy (Evisible)
An oscillation signal is an excess of νe events as a function of EνQE
Charged Current Quasi-elasticCharged Current Quasi-elastic
θ
μ or e
Beamν
N N’
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Model describes CCQE νμ data well
Kinetic Energy of muon
From Q2 fits to MB νμ CCQE data: MA
eff ─ Effective axial mass Elo
SF ─ Pauli blocking parameter
From electron scattering data: Eb ─ Binding energy pf ─ Fermi momentum
data/MC~1across all
angles & energyafter fit
Neutrino Interaction Parameters in NUANCENeutrino Interaction Parameters in NUANCE
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N
π0
N
NC π0
The π0 decays to 2 photons,which can look “electron-like” mimicking the signal...
<1% of π0 contribute to background.
NΔ
π+
N
μ+
25%
8%
CC π+
Easy to tag due to 3 subevents.Not a substantial background to the oscillation analysis.
Events Producing PionsEvents Producing Pions
The Δ also decays to a single photonwith ~0.5% probability
ν
ν
ν
Δ
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Particles Produced in the Detector and PIDParticles Produced in the Detector and PID
Muons: Produced in most CC events.Usually 2 subevent or exiting.
Electrons:Tag for νμνe CC quasi-elastic (QE) signal. 1 subevent
π0s:Can form a background if onephoton is weak or exits tank.In NC case, 1 subevent.
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Two Independent AnalysesTwo Independent Analyses
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The goal of both analyses is tominimize background while maximizing signal efficiency.
“Signal range” is approximately300 MeV < E
QE < 1500 MeV
One can then either:• look for a total excess
(“counting experiment”)• fit for both an excess and energy dependence (“energy fit”)
0 1 2 3 Neutrino Energy (GeV)
Arb
itra
ry U
nits
Signal Region
Example Signals:m2=0.4 eV2
m2=0.7 eV2
m2=1.0 eV2
Analysis ObjectivesAnalysis Objectives
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MiniBooNE is searchingfor a small but distinguishable event signature
In order to maintain blindness,Electron-like events were sequestered,
Leaving ~99% of the in-beam events available for study.
Rule for cuts to sequester events: <1 signal outside of the box
Low level information which did not allow particle-idwas available for all events.
Open Data for StudiesOpen Data for Studies
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Both analyses share these hit-level pre-cuts:
Only 1 subevent
Veto hits < 6
Tank hits > 200
And a radius precut: R<500 cm(where reconstructed radius is algorithm-dependent)
Analysis Pre-cutsAnalysis Pre-cuts
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Uses detailed, direct reconstruction of particle tracks,and ratio of fit likelihoods for the various event type hypotheses (μ, e and π0) to identify particles.
Philosophy:
This algorithm was found to have the bettersensitivity to νμνe appearance.Therefore, before unblinding,this was the algorithm chosen for the “primary result”
Analysis 1: “Track-Based” (TB) AnalysisAnalysis 1: “Track-Based” (TB) Analysis
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log(Le/L)>0 favors electron-like hypothesis
Note: photon conversions are electron-like.This does not separate e/π0.
Separation is clean at high energies where muon-like events are long.
Analysis cut was chosento maximize the νμ νe sensitivity
νe CCQE
νμ CCQEMC
Rejecting Muon-like Events Using log(LRejecting Muon-like Events Using log(Lee/L/Lμμ))
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MC
Cuts were chosen to maximize e sensitivity
Using a mass cut Using log(Le/L)
NC π0
νe CCQE
Rejecting Rejecting ππ00 Events Events
NC π0
νe CCQE
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BLI
ND
Testing e-Testing e-ππ0 0 Separation with DataSeparation with Data
1 subeventlog(Le/L)>0 (e-like)log(Le/L)<0 (π-like)mass>50 (high mass)
Signal region
eπ0
Invariant Masse π0
BLIND
Monte Carlo π0 only
log(Le/L)
γγ invariant mass
Sideband Region hasχ2 Probability of 69%
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Efficiency:
Log(Le/L) + Log(Le/L) + invariant mass
Simulated Backgrounds After Cuts
Summary of Track Based Analysis CutsSummary of Track Based Analysis Cuts
“Precuts” +
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Construct a set of low-level analysis variables which are combined to give a score to each event that is used to select electron-like events.
Philosophy:
This algorithm represents an independent cross check of the Track Based Analysis.
Analysis 2: Boosted Decision Trees (BDT)Analysis 2: Boosted Decision Trees (BDT)
Boosted decision trees, or “boosting” is a technique for pattern recognition through training (like a neural net).
In the interest of time I will focus on analysis 1 which is the primary analysis
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Errors, Constraints and SensitivityErrors, Constraints and Sensitivity
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We have two main categories of background:
mis-id
intrinsic e
Uncertainties in the background rates are among the sources of significant error in the analysis.
We have handles in the MiniBooNE data for each source of BG.
Sources of Sources of ννee Background Background
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Tying the νe background and signal predictionto the νμ flux constrains this analysis to a strict
νμ νe appearance-only search
Data/MC Track Based: 1.32 ± 0.26 Boosted Decision Tree: 1.22 ± 0.29
BDT
PredictNormalization& energy dependenceof both backgroundand signal
From the νμ CCQE
events
Determining the Misidentification Background RatesDetermining the Misidentification Background Rates
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μ → e νμ νe
π → μ νμ Measure the νμ fluxKinematics allows
connection to the π flux
Eπ (GeV)
E = 0.43 E
Once the π flux is known,the μ flux is determined
ννμμ Constraint on Intrinsic Constraint on Intrinsic ννee from Beam from Beam μμ++ Decay Decay
Eν (
GeV
)
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KK++ and Kand K00 Decay Backgrounds Decay Backgrounds
At high energies, well above the signal range, νμ and νe-like events are largely from neutrinos from kaon decay.
The events in these high energy bins are used to constrain the νe background from kaon decay.
signalrange
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ππ00 Production Constrained with MiniBooNE Data Production Constrained with MiniBooNE Data
Because this constrains the Δ resonance rate, it also constrains the rate of Δ→Nγ
Reweighting improvesagreement in other
variables, e.g.
This reduces the erroron predictedmis-identified π0s
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External events (sometimes referred to a “Dirt” events) are from ν interactions outside of the detector: Ndata/NMC = 0.99 ± 0.15
EnhancedBackgroundCuts
Cosmic Rays: Measured from out-of-beam data: 2.1 ± 0.5 events
External Sources of BackgroundExternal Sources of Background
Event Type of External Eventsafter PID cuts
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Summary of Predicted Background RatesSummary of Predicted Background Rates
Process # of Events
νe from μ decay 132
νe from K+ decay 71
νe from KL0 decay 23
νe from π decay 3
CC quasi-elastic 10
Elastic scattering 7
NC π0 62
NC Δ → Nγ 20
External (“Dirt”) events 17
Other misID 13
Beam unrelated 2
Total Background 360
Bea
m
Intr
insi
c ν e
ν μ M
isid
entif
icat
ion
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Handling Uncertainties in the AnalysesHandling Uncertainties in the Analyses
For a given source of uncertainty,
Errors on a wide rangeof parameters
in the underlying model
For a given source of uncertainty,
Errors in bins of reconstructed E
QE
and information on the correlationsbetween bins
What we begin with... ... what we need
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Optical Model UncertaintiesOptical Model Uncertainties
39 parameters must be varied
Allowed variations are set by the Michel calibration sample
To understand allowed variations,we ran 70 hit-level simulations, with randomly selected parameters.
“Multisims”
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Using Multisims to convert from parameter uncertainties to errors in E
QE bins:
For each error source,“Multisims” are generated within the allowed variationsby reweighting the standard Monte Carlo.In the case of the optical model, hit-level simulations are used.
Number of events passing cuts in 500 < EQE <600 MeV
1000 multisims forK+ production
70 multisims for the Optical Model
standard MC
Multisims Used to Convert Uncertainties to ErrorsMultisims Used to Convert Uncertainties to Errors
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Where:N is number of events passing cuts MC is standard monte carlorepresents a given multisimM is the total number of multisimsi,j are E
QE bins
Error Matrix Elements:
Total error matrixis sum from each source.
TB: νe-only total error matrixBDT: νμ-νe total error matrix
CVjj
MCViiij NNNN
ME
1
1 MC MC
BDT
Form Large Error Matrix for Uncertainties and CorrelationsForm Large Error Matrix for Uncertainties and Correlations
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Sensitivity of the Two AnalysesSensitivity of the Two Analyses
With all the errors calculated and before the box is opened, we can calculate the sensitivity of the two analyses.
The Track-based sensitivity is better, thus this becomes the pre-determined default analysis
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The Initial ResultsThe Initial Results
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Box Opening ProcedureBox Opening Procedure
After applying all analysis cuts:
1. Fit sequestered data to an oscillation hypothesis, returning no fit parameters.Return the 2 of the data/MC comparison for a set of diagnostic variables.
2. Open up the plots from step 1. The fitted signal is unreported. Plots are chosen to be useful diagnostics, without indicating if signal was added.
3. Report the 2 for a fit to E
QE , without returning fit parameters.
4. Compare EQE in data and Monte Carlo, returning the fit parameters.
At this point, the box is open.
Progress cautiously, in a
step-wise fashion
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Step 1 Step 1
Return the 2 of the data/MC comparison for a set of diagnostic variables
All analysis variables were returned with good probability except...
Track Based analysis 2 Probability for the Evisible fit was only 1%
This probability was sufficiently low to merit further consideration
12 variables are tested for Track Based46 variables are tested for Boosted Decision Tree
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In the Track Based analysis
• We re-examined our background estimatesusing sideband studies.
We found no evidence of a problem
• However, knowing that backgrounds rise at low energy,We tightened the lower Energy cut for the oscillation fit
E
QE> 475 MeV
We agreed to report events over the original full range: E
QE> 300 MeV,
……Step 1 Step 1
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Return the 2 of the data/MC comparison for a set of diagnostic variables
Parameters of the oscillation fit were not returned.
TB (EQE>475 MeV)
BDT2 probabilities returned:
12 variables 46 variables
Step 1 ReduxStep 1 Redux
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Open up the plots from step 1 for inspection
Examples ofwhat we saw:
MC contains fitted signal at an unknown level
Evisible
TB (EQE>475 MeV) BDT
fitted energy (MeV)
Evisible
2 Prob= 28%
2 Prob= 59%
Step 2 Step 2
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Report the 2 for a fit to EQE across full energy range
TB (EQE>475 MeV) 2 Probability of fit: 99%
BDT analysis 2 Probability of fit: 52%
Leading to...
Open the box...
Step 3 Step 3
Step 4Step 4
Counting experiment within 475<EQE<1250 MeV
380 events observed and 358 19 (stat) 35 (sys) events expeced
Signal significance:
0.55 σ
The Track-based νμνe Appearance-only Result:
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Error bars are only the diagonal elements oferror matrix.
Fit errors for E > 475 MeV:Normalization 9.6%Energy scale: 2.3%
Best Fit (dashed): (sin22, m2) = (0.001, 4 eV2)
Data are in good agreement with background prediction.
Track Based Energy Dependent Fit ResultTrack Based Energy Dependent Fit Result
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Under the hypothesis of νμ νe appearance-only as the oscillation mechanism
Energy fit: 475<EQE<3000 MeV
2 probability, null hypothesis: 93%
Oscillation Limits from the Track Based AnalysisOscillation Limits from the Track Based Analysis
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Boosted Decision Tree Result
Counting experiment within 300<EνQE<1600 MeV
971 events observed and 1070 33 (stat) 225 (sys) events expected
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Boosted Decision Tree EBoosted Decision Tree EννQEQE data/MC Comparison data/MC Comparison
Error bars are statistical and systematic(diagonals of matrix)
data - predicted (no osc) error
(sidebands used for constraint not shown)
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Boosted Decision Tree analysis shows no evidence for νμ νe appearance-only oscillations.
The two independent analyses are in good agreement.
Oscillation Limits from the BDT AnalysisOscillation Limits from the BDT Analysis
solid: Track baseddashed: Boosted Decision Tree
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96 ± 17 ± 20 eventsabove expectation,in the range of 300<Eν
QE<475MeV
Deviation: 3.7σ (preliminary)
to E>475 MeV
Background-subtracted
What About the Low Energy Region (E<475 MeV)?What About the Low Energy Region (E<475 MeV)?
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Best Fit (dashed): (sin22, m2) = (1.0, 0.03 eV2)2 Probability: 18%
Fit to the > 300 MeV range:
Examples in LSND allowedrange
Are the Excess Events Consistent with Oscillations?Are the Excess Events Consistent with Oscillations?
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This will be addressed by further study
This discrepancy is interesting, but requires further investigation
A two-neutrino appearance-only model systematically disagrees with the shape as a function of energy.
We need to investigate non-oscillation explanations, including unexpected behavior of low energy cross sections.This will be relevant to future e searches
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A MiniBooNE-LSND Compatibility TestA MiniBooNE-LSND Compatibility Test
• For each m2, determine the MB and LSND measurement:
zMB zMB, zLSND zLSND where z = sin2(2) and z is the 1 error
• For each m2, form 2 between MB and LSND measurement
• Find z0 that minimizes 2
(weighted average of two measurements) and this gives 2min
• Find probability of 2min for 1 dof;
this is the joint compatibility probability for this m2
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0.001
0.010
0.100
0.25 0.75 1.25 1.75 2.25 2.75
dm2 (eV2)
Jo
int
MB
-LS
ND
Pro
b (
1d
of)
2% Compatibility
Max
imum
Joi
nt P
roba
bilit
y
m2 (eV2)
MiniBooNE is incompatible with a νμνe appearance only interpretation of LSND
at 98% CL
A MiniBooNE-LSND Compatibility TestA MiniBooNE-LSND Compatibility Test
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Future PlansFuture Plans
A paper on this analysis has been posted to the archive and will soon be sent to PRL.
Many more papers supporting this analysis will follow, in the very near future:
νμ CCQE productionπ0 productionMiniBooNE-LSND-Karmen joint analysis
We are pursuing further analyses of the neutrino data,including...
an analysis which combines TB and BDT,more exotic models for the LSND effect.
MiniBooNE is presently taking data in antineutrino mode.
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Today I’ve shown an analysis of MiniBooNE data within a νμνe appearance-only context
ConclusionsConclusions
The observed reconstructed energy distribution is inconsistent with oscillations in a νμνe appearance-only model
The limit set is compatible with LSND at a level of 2% or less.
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Within the energy range defined by this oscillation analysis, the event rate is consistent with background.
The observed low energy deviation is under investigation.
ConclusionsConclusions
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QuestionsQuestions
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10% Photocathode coverage
Two types of Hamamatsu Tubes: R1408, R5912
Charge Resolution: 1.4 PE, 0.5 PE
Time Resolution 1.7 ns, 1.1ns
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Each event is characterized by 7 reconstructed variables:
vertex (x,y,z), time, energy, and direction ()(Ux, Uy, Uz).
Resolutions: vertex: 22 cm
direction: 2.8 energy: 11%
CCQE events
2 subeventsVeto Hits<6Tank Hits>200
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Fundamental information from PMTsAnalysis Hit Position Charge Hit Timing variablesEnergy
Time sequence
Event shape
Physics
Step 1:Convert the “Fundamental information”
into “Analysis Variables”
“Physics” = 0 mass, EQE, etc.
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Resolutions:vertex: 24 cmdirection: 3.8ºenergy 14%
Examples of “Analysis Variables”
Reconstructed quantities which are inputs to EQE
CCQE CCQE
UZ = coszEvisible
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Step 2: Reduce Analysis Variables to a Single PID Variable
“A procedure that combines many weak classifiersto form a powerful committee”
Boosted Decision Trees
hit level(charge, time,
position)
analysis variables
One singlePID “score”
Byron P. Roe, et al., NIM A543 (2005) 577.
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(sequential series of cuts based on MC study)
A Decision Tree
(Nsignal/Nbkgd)
30,245/16,305
9755/23695
20455/3417 9790/12888
1906/11828 7849/11867
signal-likebkgd-like
bkgd-like sig-like
sig-likebkgd-like
etc.
This tree is one of many possibilities...
Variable 1
Variable 2
Variable 3
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A set of decision trees can be developed,each re-weighting the events to enhance identification of backgrounds misidentifiedby earlier trees (“boosting”)
For each tree, the data event is assigned +1 if it is identified as signal,-1 if it is identified as background.
The total for all trees is combined into a “score”
negative positiveBackground-
like signal-like
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BDT cuts on PID score as a function of energy.We can define a “sideband” just outside of the signal region
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BDT cuts on PID score as a function of energy.We can define a “sideband” just outside of the signal region
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BDT Efficiency and backgrounds after cuts:
Analysis cuts on PID score as a function of Energy
signal
background
Efficiency after precuts
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Other Single Photon Sources
From Efrosinin, hep-ph/0609169, calculation checked by Goldman, LANL
Neutral Current: + N + N +
Charged Current
+ N + N’ +
negligible
where the presence of the leads to mis-identification
Use events where the is tagged by the michel e-,
study misidentification using BDT algorithm.
< 6 events @ 95% CL
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Flux from +/+ decay 6.2 / 4.3 √ √ Flux from K+ decay 3.3 / 1.0 √ √ Flux from K0 decay 1.5 / 0.4 √ √ Target and beam models 2.8 / 1.3 √
-cross section 12.3 / 10.5 √ √ NC 0 yield 1.8 / 1.5 √
External interactions (“Dirt”) 0.8 / 3.4 √ Optical model 6.1 / 10.5 √ √ DAQ electronics model 7.5 / 10.8 √
Source of UncertaintyOn e background
Checked or Constrained by MB data
Furtherreduced by
tyinge to
Track Based/Boosted Decision Treeerror in %
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MAQE, elo
sf 6%, 2% (stat + bkg only) QE norm 10% QE shape function of E
e/ QE function of E
NC 0 rate function of 0 mom MA
coh, coh ±25% Nrate function of mom + 7% BF
EB, pF 9 MeV, 30 MeVs 10% MA
1 25% MA
N 40% DIS 25%
determined fromMiniBooNE QE data
determined fromMiniBooNE NC 0 data
Example: Cross Section Uncertainties
determined from other experiments
(Many are common to and e and cancel in the fit)
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How the Constraints Enter
TB: Reweight MC prediction to match measured νμ result (accounting for systematic error correlations)
Two Approaches
Systematic (and statistical) uncertainties are included in (Mij)-1
BDT: Include the correlations of νμ to νe in the error matrix:
(i,j are bins of EQE)
4/19/20074/19/2007Jonathan LinkJonathan Link
1) There are various waysto present limits:
• Single sided raster scan(historically used, presented here)
• Global scan• Unified approach
(most recent method)
2) This result must befolded into an LSND-Karmenjoint analysis.
We will present a full joint analysis soon.
Two points on interpreting our limit
Church, et al., PRD 66, 013001