first results from miniboone eric prebys, fnal/boone collaboration

First Results from MiniBooNEFirst Results from MiniBooNE

Eric Prebys, FNAL/BooNE Collaboration

HEP Seminar, UT Austin, April 30th, 2007 – E. Prebys 2

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


OutlineOutline

State of neutrino mixing measurements History and background Without LSND LSND and Karmen

Experiment Beam Detector Calibration and cross checks Analysis

Results Future Plans and outlook


The Neutrino “Problem”The Neutrino “Problem”

1968: Experiment in the Homestake Mine first observes neutrinos from the Sun, but there are far fewer than predicted. Possibilities: Experiment wrong? Solar Model wrong? ( believed by most not involved) Enough created, but maybe oscillated (or decayed to

something else) along the way. ~1987: Also appeared to be too few atmospheric muon

neutrinos. Less uncertainty in prediction. Similar explanation.

Both results confirmed by numerous experiments over the years.

1998: SuperKamiokande observes clear oscillatory behavior in signals from atmospheric neutrinos. For most, this establishes neutrino oscillations “beyond a reasonable doubt”


Theory of Neutrino OscillationsTheory of Neutrino Oscillations

Neutrinos are produced and detected as weak eigenstates (e ,, or ). These can be represented as linear combination of mass eigenstates. If the above matrix is not diagonal and the masses are not equal, then the net weak

flavor content will oscillate as the neutrinos propagate. Example: if there is mixing between the e and :

then the probability that a e will be detected as a after a distance L is:

E

LmP e

222 27.1sin2sin)(

2

1

cossin

sincos

e

)eV(in 221

22 mm

Distance in km

Energy in GeV

Mass eigenstatesFlavor eigenstates

Only measure magnitude of the difference of the squares of the masses.


Probing Neutrino Mass DifferencesProbing Neutrino Mass Differences

& Reactors

Accelerators use decay to directly probe e

Reactors use use disappearance to probe e Cerenkov detectors directly measure and e content in atmospheric neutrinos. Fit to e mixing hypotheses

Solar neutrino experiments typically measure the disappearance of e.

Different experiments probe different ranges of E

L Path length

Energy

Also probe with new generation of “long baseline” accelerator and reactor experiments, MINOS, T2K, etc


Best Three Generation Picture (all experiments but LSND)Best Three Generation Picture (all experiments but LSND)


The LSND Experiment (1993-1998)The LSND Experiment (1993-1998)

Signature Cerenkov ring from

electron Delayed from

neutron capture

~30 m

Energy 20-50 MeV

e

e

e

mix

nepe


LSND ResultLSND Result

(Soudan, Kamiokande,MACRO, Super-K)

(Homestake, SAGE,GALLEX, Super-KSNO, KamLAND)

Excess Signal: Best fit:

Only exclusive appearance result to date Problem: m2 ~ 1 eV2 not consistent with other

results with simple three generation mixing


PossibilitiesPossibilities

4 neutrinos? We know from Z lineshape there are only 3 active

flavors Sterile?

CP or CPT Violation? More exotic scenarios? LSND Wrong? Can’t throw it out just because people don’t like it.


m5

3+2 models

hep-ph/0305255

Accommodating a Positive SignalAccommodating a Positive Signal

We know from LEP that there are only 3 active, light neutrino flavors.

If MiniBooNE confirms the LSND results, it might be evidence for the existence of sterile neutrinos


Karmen II Experiment: not quite enoughKarmen II Experiment: not quite enough

Pulse 800 MeV proton beam (ISIS) 17.6 m baseline 56 tons of liquid scintillator Factor of 7 less statistical reach than

LSND -> NO SIGNAL Combined analysis still leaves an

allowed region

Combined


Role of MiniBooNERole of MiniBooNE

Boo(ster) N(eutrino) E(xperiment) Full “BooNE” would have two detectors

Primary Motivation: Absolutely confirm or refute LSND result Optimized for L/E ~ 1 Higher energy beam -> Different systematics than LSND

• E: ~30 MeV -> 500 MeV• L: ~30 m -> 500 m

Timeline Proposed: 12/97 Approved 1998 Began Construction: 10/99 Completed: 5/02 First Beam: 8/02 Began to run concurrently with NuMI: 3/05 Oscillation results: 4/07


MiniBooNE Neutrino Beam (not to scale)MiniBooNE Neutrino Beam (not to scale)

8 GeV Protons ~ 8E16 p/hr max ~ 1 detected neutrino/minute L/E ~ 1

FNALBooster Be

Targetand Horn

“Little Muon Counter” (LMC): to understand K flux

50 m Decay Region

500m dirt

Detector


DetectorDetector

950,000 l of pure mineral oil 1280 PMT’s in inner region 240 PMT’s outer veto region Light produced by Cerenkov radiation and

scintillation

Light barrier

Trigger:All beam spillsCosmic ray triggersLaser/pulser triggersSupernova trigger


Neutrino Detection/Particle IDNeutrino Detection/Particle ID

e

e

n

p

W

e

e

n

p

W

n 0

Z

p 0

e

Important Background!!!


Selecting Neutrino EventsSelecting Neutrino Events

Collect data from -5 to +15 usec around each beam spill trigger.

Identify individual “events” within this window based on PMT hits clustered in time.

Time (ns) Time (ns) Time (ns)

No cuts Veto hits < 6 Veto hits<6tank hits>200

1600 ns spill


Delivering ProtonsDelivering Protons

Requirements of MiniBooNE greatly exceed the historical performance of the 30+ year old 8 GeV Booster, pushes… Average repetition rate Above ground radiation Radiation damage and

activation of accelerator components

Intense Program to improvethe Booster Shielding Loss monitoring and analysis Lattice improvements (result of Beam Physics involvement) Collimation system

Very challenging to continue to operate 8 GeV line during NuMI/MINOS operation Once believed impossible Element of lab’s “Proton Plan” Goal to continue to deliver roughly 2E20 protons per years to the

8 GeV program even as NuMI intensities ramp up.


Small but HungrySmall but Hungry

MiniBooNE began taking data in Fall of 2002, and has currently taken more protons than all other users in history of Fermilab combined (including NuMI and pBar)

MiniBooNE, 86.2

All others (1974-present), 49.2

NuMI, 30.1

Total protons (1019)

63E19 Events in mode (this analysis)

In anti- mode since ~1/06


Modeling neutrino flux Modeling neutrino flux

Production GEANT4 model of target,

horn, and beamline MARS for protons and

neutrons Sanford-Wang fit to

production data for and K Mesons allowed to decay in

model of decay pipe. Retain neutrinos which point

at target Data Constrained by HARP

Experiment

e e

K e e

K

Antineutrino content: 6% e/ = 0.5%

“Intrinsic” e + e sources: e+ e (52%)

K+ e+ e (29%)K0 e e (14%)

Other ( 5%)


InteractionsInteractions

Cross sections Based on NUANCE 3 Monte Carlo

• Use NEUT and NEUEN as cross checks

Theoretical input:• Llewellyn-Smith free nucleon

cross sections• Rein-Sehgal resonant and

coherent cross-sections• Bodek-Yang DIS at low-Q2• Standard DIS parametrization at

high Q2• Fermi-gas model• Final state interaction model

Constraining NUANCE Observed nm data

• MAeff -- effective axial mass

• EloSF -- Pauli Blocking parameter

From electron scattering data• Eb -- binding energy• pf -- Fermi momentum

MiniBooNE


D. Casper, NPS, 112 (2002) 161

Predicted Event Rates (NUANCE)Predicted Event Rates (NUANCE)


Characterizing the DetectorCharacterizing the Detector

Full GEANT 3.21 model of detector Includes detailed (39-parameter!!) optical

model of oil and PMT response MC events produced at the raw data level and

reconstructed in the same way as real events


Calibrating the DetectorCalibrating the Detector


N 0

N

NC0

The 0 decays to 2 photons,which can look “electron-like” mimicking the signal...

<1% of 0 contribute to background.

N

N

25%

8%

CC+

Easy to tag due to 3 subevents.Not a substantial background to the oscillation analysis.

Events producing pions

(also decays to a single photon with 0.56% probability)


Bottom line: signal and background Bottom line: signal and background

If the LSND best fit is accurate, only about a third of our observed rate will come from oscillations

Backgrounds come from both intrinsic e and misidentified

SignalMis IDIntrinsic e

Energy distribution can help separate


Analysis PhilosophyAnalysis Philosophy

The “golden signal” in the detector is the Charged Current Quasi-Elastic (CCQE) event With a particular mass hypothesis, the neutrino energy can be reconstructed from the observed partcle with

If the LSND signal were due to neutrino oscillations, we would expect an excess in the energy range of 300-1500 MeV

The “signal” is therefore an event with A high probability of being a e CCQE event A reconstructed energy in the range 300 to 1500 MeV

cose or

eor

N N


Two AnalysesTwo Analyses

“Track Based” analysis: Use detailed tracking information to form a particle

ID likliehood. “Box” defined by e likelihood and energy.

Backgrounds weighted by observed events outside of box.

“Boosting” Particle ID based on feeding a large amount of event

information into a “boosted decision tree” (details to follow)

“Box” defined by the boosted “score” and the mass range. Backgrounds determined by models which are largely constrained by the data.


Common Event CutsCommon Event Cuts

>200 tank hits <6 veto hits

R<500 cm (algorithm dependent)

dataMC


BlindnessBlindness

In general, the two analyses have sequestered (hidden) data which has A reconstructed neutrino energy in the range 300 to

1500 MeV A high probability of being an electron

This leaves the vast majority (99%) of the data available for analysis

Individual open boxes were allowed, provided it could be established that an oscillation would not lead to a significant signal.

In addition, detector level data (hit times, PMT spectra, etc) were available for all events.

Determination of “primary” analysis based on final sensitivity before opening the box


The goal of both analyses: minimize background & maximize signal efficiency.

“Signal range” is approximately300 MeV < EQE < 1500 MeV

One can then either:• look for a total excess

(“counting expt”)• fit for both an excess and

energy dependence (“energy fit”)

MiniBooNE signal examples:m2=0.4 eV2

m2=0.7 eV2

m2=1.0 eV2

0 1 2 3 Neutrino Energy (GeV)

Arb

itra

ry U

nits


MiniBooNE is searchingfor a small but distinctive 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 Studies:


Uses detailed, direct reconstruction of particle tracks,and ratio of fit likelihoods to identify particles.

Philosophy:

This algorithm was found to have the bettersensitivity to e appearance.

Therefore, before unblinding, this was the algorithm chosen for the “primary result”

Analysis 1: “Track-Based” (TB) Analysis


Track-based analysisTrack-based analysis


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


Rejecting “muon-like” eventsUsing log(Le/L)

log(Le/L)>0 favors electron-like hypothesis

Note: photon conversions are electron-like.This does not separate e/0.

Separation is clean at high energies where muon-like events are long.

Analysis cut was chosento maximize the e sensitivity

e CCQE

CCQEMC


Rejecting “0-like” events

MC

Cuts were chosen to maximize e sensitivity

Using a mass cut Using log(Le/L)

NC0

e CCQE NC0

e CCQE


BLI

ND

eπ0

Invariant Masse π0

BLIND

Monte Carlo π0 only

Testing e-0 separation using data

1 subeventlog(Le/L)>0 (e-like)log(Le/L)<0 (-like)mass>50 (high mass)

log(Le/L)

invariant masssignal


2 Prob for mass<50 MeV (“most signal-like”): 69%

mass<200 (low mass)log(Le/L)>0 (e-like)log(Le/L)<0 (-like)

BLIND

Monte Carlo π0 only

Next: look here....

1 subeventlog(Le/L)>0 (e-like)log(Le/L)<0 (-like)mass<200 (low mass)


Efficiency:

Log(Le/L) + Log(Le/L) + invariant mass

Backgrounds after cuts

Summary of Track Based cuts

“Precuts” +


Construct a set of low-level analysis variables which are used to make a series of cuts to

classify the events.

Philosophy:

This algorithm represents an independent cross check of the Track Based Analysis

Analysis 2: Boosted Decision Trees (BDT)


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.


Resolutions:vertex: 24 cmdirection: 3.8ºenergy 14%

Examples of “Analysis Variables”

Reconstructed quantities which are inputs to EQE

CCQE CCQE

UZ = coszEvisible


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.


(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-like bkgd-like

etc.

This tree is one of many possibilities...

Variable 1

Variable 2

Variable 3


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


BDT cuts on PID score as a function of energy.We can define a “sideband” just outside of the signal region


BDT Efficiency and backgrounds after cuts:

Analysis cuts on PID score as a function of Energy

signal

background

Efficiency after precuts


Errors, Constraints and Sensitivity


We have two categories of backgrounds:

(TB analysis)

mis-id

intrinsic e

Predictions of the backgrounds are among the nine sources of significant error in the analysis


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 %


Tying the e background and signal predictionto the flux constrains this analysis to a strict

e appearance-only search

Data/MC Boosted Decision Tree: 1.22 ± 0.29 Track Based: 1.32 ± 0.26

BDT

Predict

Normalization& energy dependenceof both backgroundand signal

From the CCQE

events


e e

• Measure the flux• Kinematics allows

connection to the flux

E(GeV)

E(GeV)

E = 0.43 E

• Once the flux is known, the flux is determined

constraint on intrinsic e from + decay chains


K+ and K0 decay backgrounds

At high energies, above “signal range” and “e -like” events arelargely due to kaon decay

Signal examples:m2=0.4 eV2

m2=0.7 eV2

m2=1.0 eV2

Predicted rangeof significant oscillation signal:300<EQE<1500 MeV

0 1 2 3 Neutrino Energy (GeV)

Arb

itra

ry U

nitssignal

range


TB

In Boosted DecisionTree analysis:Low energy bin (200<EQE<300 MeV)

constrains mis-ids:0, N, dirt ...

signal range

signal

up to 3000 MeV

BDT

In both analyses,high energy bins constrain

e background

Use of low-signal/high-background energy bins


We constrain 0 production using data from our detector

Because this constrains the resonance rate, it also constrains the rate of N

Reweighting improvesagreement in other

variables, e.g.

This reduces the erroron predictedmis-identified 0s


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


“Dirt” Events

Event Type of Dirt after PID cutsEnhancedBackgroundCuts

interactions outside of the detector Ndata/NMC = 0.99 ± 0.15

Cosmic Rays: Measured from out-of-beam data: 2.1 ± 0.5 events

External Sources of Background


Summary of predicted backgrounds forthe final MiniBooNE result(Track Based Analysis):

(example signal)


Handling 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 EQE

and information on the correlationsbetween bins

What we begin with... ... what we need


How the constraints enter...

TB: Reweight MC prediction to match measured result (accounting for systematic error correlations)

Two Approaches

Systematic (and statistical) uncertainties are included in (Mij)-1

BDT: include the correlations of to e in the error matrix:

(i,j are bins of EQE)


MAQE, elo

sf 6%, 2% (stat + bkg only) QE norm 10% QE shape function of Ee/ 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)


Example:Optical 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 differing parameters.

“Multisims”


Using Multisims to convert from errors on parameters to errors in EQE bins:

For each error source,“Multisims” are generated within the allowed variations

by reweighting the standard Monte Carlo.In the case of the OM, hit-level simulations are used.

number of multisims

Number of events passing cuts in bin 500<EQE<600 MeV

1000 multisims forK+ production

70 multisims for the Optical Model

standard MC


Correlations between EQE bins from

the optical model:

• N is number of events passing cuts •MC is standard monte carlo• represents a given multisim• M is the total number of multisims• i,j are EQE bins

Error Matrix Elements:

Total error matrixis sum from each source.

TB: e-only total error matrixBDT: -e total error matrix

CVjj

MCViiij NNNN

ME

1

1 MC MC

BDT


As we show distributions in EQE,keep in mind that error bars are

the diagonals of the error matrix.

The effect of correlations between EQE binsis not shown,

however EQE bin-to-bin correlations improve the sensitivity to oscillations,

which are based on an energy-dependent fit.


Sensitivity of the two analyses

The Track-based sensitivity is better,thus this becomes the pre-determined default algorithm

Set using2=1.64 @ 90% CL


Comparison to sensitivity goal for 5E20 POTdetermined by Fermilab PAC in 2003


The Initial Results


Box 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 Monte Carlo has unreported signal.Plots chosen to be useful diagnostics, without indicating if signal was added.

3. Report the 2 for a fit to EQE , without returning fit parameters.

4. Compare EQE in data and Monte Carlo, returning the fit parameters.At this point, the box is open (March 26, 2007)

5. Present results two weeks later.

Progress cautiously, in a

step-wise fashion


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 of Evisible fit: 1%

This probability was sufficiently low to merit further consideration

12 variables are tested for TB46 variables are tested for BDT


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 cuts for the oscillation fit:

EQE> 475 MeV

We agreed to report events over the original full range: EQE> 300 MeV,


Step 1: again!

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)BDT

2 probabilities returned:

12 variables 46 variables


Open up the plots from step 1 for approval.

Examples ofwhat we saw:

MC contains fitted signal at unknown level

Step 2

Evisible

TB (EQE>475 MeV) BDT

fitted energy (MeV)

Evisible

2 Prob= 28%

2 Prob= 59%


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%

Step 3

Leading to...

Open the box...

Step 4


Counting Experiment: 475<EQE<1250 MeV

data: 380 eventsexpectation: 358 19 (stat) 35 (sys) events

significance: 0.55

The Track-based e Appearance-only Result:


Error bars arediagnonals oferror matrix.

Fit errors for >475 MeV:Normalization 9.6%Energy scale: 2.3%

Best Fit (dashed): (sin22, m2) = (0.001, 4 eV2)

Track Based energy dependent fit results:Data are in good agreement with background prediction.


The result of the e appearance-only analysis

is a limit on oscillations:

Energy fit: 475<EQE<3000 MeV

2 probability, null hypothesis: 93%


96 ± 17 ± 20 eventsabove background,for 300<EQE<475MeV

Deviation: 3.7

As planned before opening the box....Report the full range: 300<EQE<3000 MeV

to E>475 MeV

Background-subtracted:


Best Fit (dashed): (sin22, m2) = (1.0, 0.03 eV2)2 Probability: 18%

Fit to the > 300 MeV range:

}Examples in LSND allowedrange


This will be addressed by MiniBooNE and SciBooNE

This 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


Boosted Decision Tree Analysis

Counting Experiment: 300<EQE<1600 MeV data: 971 eventsexpectation: 1070 33 (stat) 225 (sys) events

significance: 0.38


Boosted Decision Tree EQE data/MC comparison:

error bars arestat and sys(diagonals of matrix)

data -predicted (no osc) error

(sidebands used for constraint not shown)


Boosted Decision Tree analysis shows no evidence for e appearance-only oscillations.

Energy-fit analysis:solid: TBdashed: BDT

Independent analysesare in good agreement.


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


A 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


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

bili

ty

m2 (eV2)

MiniBooNE is incompatible with a e appearance only interpretation of LSND

at 98% CL


Plans:

A paper on this analysis will be posted to the “archive” and to the MiniBooNE webpage after 5 CT today.

Many more papers supporting this analysis will follow, in the very near future:

CCQE production0 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.


Conclusions


• A generic search for a e excess in our beam,

• An analysis of the data within a e appearance-only context

Our goals for this first analysis were:


Within the energy range defined by this oscillation analysis, the event rate is consistent with background.

The observed low energy deviation is under investigation.


The observed reconstructed energy distribution is inconsistent with a e appearance-only model

Therefore we set a limit on e appearance


We Thank:

DOE and NSF

The Fermilab Divisions and Staff


Conclusions and OutlookConclusions and Outlook

MiniBooNE has been running for over three years, and continues to run well in the NuMI era

The analysis tools are well developed and being refined to achieve the quality necessary to release the result of our blind analysis

Recent results for CCQE and CCPiP give us confidence on our understanding of the detector and data.

Look forward to many interesting results in 2006

Backup SlidesBackup Slides


Sterile Neutrinos: Astrophysics Constraints (courtesy M. Sterile Neutrinos: Astrophysics Constraints (courtesy M. Shaevitz)Shaevitz)

Constraints on the number of neutrinos from BBN and CMB Standard model gives

N=2.6±0.4 constraint

If 4He systematics larger, then N=4.0±2.5

If neutrino lepton asymmetry or non-equilibrium, then the BBN limit can be evaded.K. Abazajian hep-ph/0307266G. Steigman hep-ph/0309347

“One result of this is that the LSND result is not yet ruled out by cosmological observations.” Hannestad astro-ph/0303076

Bounds on the neutrino masses also depend on the number of neutrinos (active and sterile) Allowed mi is 1.4 (2.5) eV

4 (5) neutrinos

N=3 solid 4 dotted 5 dashed

Hannestad astro-ph/0303076

0.00 0.005 0.01 0.015 0.02 0.025 0.03


Reactor Experimental ResultsReactor Experimental Results

Single reactor experiments (Chooz, Bugey, etc). Look for e disappearance: all negative

KamLAND (single scintillator detector looking at ALL Japanese reactors): e disappearance consistent with mixing.


K2KK2K

First “long baseline” accelerator experiment Beam from KEK PS to Kamiokande, 250 km away Look for disappearance (atmospheric “problem”)

Results consistent with mixing

Best fit

No mixing

Allowed Mixing Region


HARP (CERN) 5% Beryllium target 8.9 GeV proton beam momentum

Modeling Production of Secondary Pions

HARP collaboration,hep-ex/0702024

Data are fit to a Sanford-Wangparameterization.


Everybody Loves a MysteryEverybody Loves a Mystery

3+2 Sterile neutrinos Sorel, Conrad, and Shaevitz (hep-ph/0305255)

MaVaN & 3+1 Hung (hep-ph/0010126)

Sterile neutrinos Kaplan, Nelson, and Weiner (hep-ph/0401099)

• Explain Dark Energy? CPT violation and 3+1 neutrinos

Barger, Marfatia & Whisnant (hep-ph/0308299)• Explain matter/antimatter asymmetry

Lorentz Violation Kostelecky & Mewes (hep-ph/0406035)

Extra Dimensions Pas, Pakvasa, & Weiler (hep-ph/0504096)

Sterile Neutrino Decay Palomares-Ruiz, Pascoli & Schwetz (hep-ph/0505216)


Three Generation Mixing (Driven by experiments listed)Three Generation Mixing (Driven by experiments listed)

General Mixing Parameterization

231312231312231223131223

231312231312231223131223

1312131213

ccescscseccsss

scesssccecsssc

essccc

ii

ii

i

CP violating phase

Almost diagonal Third generation weakly

coupled to first two “Wolfenstein Parameterization”

Mixing large No easy simplification Think of mass and weak eigenstates as totally separate


SNO Solar Neutrino ResultSNO Solar Neutrino Result

Looked for Cerenkov signals in a large detector filled with heavy water. Focus on 8B neutrinos Used 3 reactions:

e+dp+p+e-: only sensitive to e

x+dp+n+x: equally sensitive to e , , x+ e- x+ e-: 6 times more sensitive to e than ,d

Consistent with initial full SSM flux of e’s mixing to , Completely consistent with three generation mixing

Just SNO SNO+others

34.tan;eV105 :Favor 2252 Δm

first results from miniboone eric prebys, fnal/boone collaboration

Documents

hep seminar

ut austin

lsnd slide

lsnd lsnd

outlook slide

lsnd experiment

e reactors

e content