Zelimir Djurcic Physics Department Columbia University Status of MiniBooNE Status of...

Zelimir DjurcicZelimir Djurcic

Physics DepartmentPhysics Department

Columbia UniversityColumbia University

Status of MiniBooNEStatus of MiniBooNEExperimentExperiment

WIN07, CalcuttaWIN07, Calcutta

January 15-20,2007January 15-20,2007

MiniBooNE consists of about 70 scientists from 16 institutions.

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 MiniBooNE CollaboratioCollaboratio

nn

Zelimir Djurcic-WIN2007

Before MiniBooNEBefore MiniBooNE


LSND took data from 1993-98 - 49,000 Coulombs of protons - L = 30m and 20 < E< 53 MeV

Saw an excess ofe :87.9 ± 22.4 ± 6.0 events.

With an oscillation probability of (0.264 ± 0.067 ± 0.045)%.

3.8 significance for excess.

ee

eOscillations?

Before MiniBooNE: The LSND Before MiniBooNE: The LSND ExperimentExperiment

Signal: p e+ n

n p d (2.2MeV)

e


Kamioka, IMB, Super K, Soudan II, Macro, K2Km2 = 2.510-3 eV2

Homestake, Sage, Gallex, Super-KSNO, KamLAND m2 = 8.210-5 eV2

This signal looks very differentfrom the others...• Much higher m2 = 0.1 – 10 eV2 • Much smaller mixing angle• Only one experiment!

Current Oscillation Current Oscillation StatusStatus

In SM there are only 3 neutrinos

m13

m12

m23

2 2 2

2 2 221 32 31

Three distinct neutrino oscillation signals,

with

For three neutrinos,

expect

solar atm LSNDm m m

m m m


• Want the same L/E• Want higher statistics• Want different systematics• Want different signal signature and

backgrounds

Fit to oscillation hypothesis

Backgrounds

Confirming or Refuting Confirming or Refuting LSNDLSND

Need definitive study of e at high m2 … MiniBooNE


MiniBooNEMiniBooNE

((BooBoosterster N Neutrinoeutrino E Experiment)xperiment)


magnetic horn: meson focusing

decay region: , K

absorber: stops undecayed mesons

“little muon counters:” measure K flux

in-situ

→

e?

50 m decay pipe

magnetic focusing horn

FNAL 8 GeV Beamline

Search for Search for ee appearance in appearance in beambeam

e e ??????

Use protons from the 8 GeV booster Neutrino Beam <E>~ 1 GeV

MiniBooNE MiniBooNE Detector:Detector:12m diameter 12m diameter spheresphere950000 liters of 950000 liters of oil oil (CH2)1280 inner PMTs1280 inner PMTs240 veto PMTs240 veto PMTs


Few words on:Few words on:-Neutrino Flux-Neutrino Flux-Cross-section-Cross-section

-Detector Modeling-Detector Modeling

• mainly from • <E> ~ 700 MeV

predicted flux

intrinsic e

• ~10-3

• + e+ e

• K+ 0 e+ e (also KL)

Flux at MiniBooNE Flux at MiniBooNE DetectorDetectorFlux simulation uses Geant4 Monte Carlo

Meson production is based on Sanford-Wang parameterization of p-Be interaction cross-section.

p

• E910: , K production @ 6, 12, 18 GeV

w/thin Be target• HARP: , K production @ 8 GeV w/ 5, 50, 100% thick Be target

MINOS, NuMIK2K, NOvAMiniBooNE, T2K

Super-K atmospheric

Predictions from NUANCE

- 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

Low Energy Low Energy Cross Cross SectionsSections

Imperative is to preciselypredict signal & bkgd ratesfor future oscillation experiments

We need data on nuclear targets!(most past data on H2, D2)Current cross-section studies devoted to understanding Current cross-section studies devoted to understanding

of the backgrounds in the MiniBooNE appearance signal.of the backgrounds in the MiniBooNE appearance signal.


Neutrino Interactions in the Neutrino Interactions in the DetectorDetector

e n e- pWe are looking for We are looking for e e

::Current Collected data:700k neutrino candidates (before analysis cuts) for 7 x 1020 protons on target (p.o.t.)

If LSND is correct, we If LSND is correct, we expect several hundred expect several hundred ee (after analysis cuts) from (after analysis cuts) from for for e e oscillations.oscillations.

- 48% QE- 31% CC +

- 1% NC elastic- 8% NC 0

- 5% CC 0

- 4% NC +/-

- 4% multi-

NUANCE MC generator NUANCE MC generator converts the flux into converts the flux into event rates in event rates in MiniBooNE detectorMiniBooNE detector


Detector ModelingDetector Modeling

Detector (optical) model defines how light of generated event is propagated and detected in MiniBooNE detector

Sources of light: Cerenkov (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).

Michel electrons from decay: provide E calibration at low energy (52.8 MeV), good monitor of light transmission, electron PID

0 mass peak: energy scale & resolutionat medium energy (135 MeV), reconstruction

We have calibration sources spanning wide range of energies and all event types !

12% E res at

52.8 MeV

Energy CalibrationEnergy Calibration

cosmic ray + tracker + cubes: energy scale & resolution at high energy (100-800 MeV), cross-checks track reconstruction

provides tracks of known length → E

e

PRELIMINARYPRELIMINARY


How to Detect and ReconstructHow to Detect and ReconstructNeutrino EventsNeutrino Events


Main trigger is an accelerator signal indicating a beam spill.Information is read out in 19.2 s interval covering arrival of beam and requests of various triggers (laser, random strobe, cosmic…).

Detector OperationDetector Operation

The rate of neutrinocandidates was constant:1.089 7 x 10-15 /P.O.T.


Detector Operation and Event Detector Operation and Event reconstructionreconstruction

To reconstruct an event:-Separate hits in beam window by time into sub-events of related hits

-Reconstruction package maximizes likelihood of observed charge and time distribution of PMT hits to find track position, direction and energy (from the charge in the cone) for each sub-event

No high level analysis needed to see neutrino events

Backgrounds: cosmic muons and decay electrons

->Simple cuts reduce non-beam backgrounds to ~10-3

Electronics continuously record charges and times of PMT hits.


0 →

Michel e-

candidate

beam candidate

beam 0

candidate

Čerenkov rings provide primary means of identifying products of interactions in the detector

n - p

e n e- p

p p 0

n n

Particle IdentificationParticle Identification

Particle Identification IIParticle Identification II

Search for oscillation

e n e- p

events is by detection of single electron like-rings, based on Čerenkov ring profile.

muon

Angular distributions of PMT hits relative to track direction:

electron

PRELIMINARY

PRELIMINARY

Signal Separation from Signal Separation from BackgroundBackground

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

0→Signal N

Search for O(102) e oscillation events in O(105) unoscillated events

Backgrounds


Two complementary approaches

for reducible background

“Simple” cuts+Likelihood: easy to understand

Boosted decision trees: maximize sensitivity

Background Rejection and Blind Background Rejection and Blind AnalysisAnalysis

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)


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).

Boosting PID AlgorithmBoosting PID Algorithm

Boosted Decision Trees at MiniBooNE:Use about 200 input variables to train the trees-target specific backgrounds-target all backgrounds generically

Boosting Decision Tree

Muons Electrons


Reference NIM A 543 (2005) 577.Reference NIM A 543 (2005) 577.


Likelihood ApproachLikelihood Approach

Apply likelihood fits to three Apply likelihood fits to three hypotheses:hypotheses:-single electron track-single electron track-single muon track-single muon track-two electron-like rings (-two electron-like rings (0 0 event event hypothesis )hypothesis )Form likelihood differences using minimized –logL

quantities: log(Le/L) and log(Le/L)

Compare observed light distribution to fit prediction:

Does the track actually look like an electron?

log(Le/L)

log(Le/L)<0 -like events

log(Le/L)>0 e-like events



CCQE and CCQE and 00 Analysis Analysis

MiniBooNE Quasi-Elastic MiniBooNE Quasi-Elastic DataData

epne pn

12C-

beam

ell

llQE

PEM

mMEE

cos

2

2

1 2

l

CCQE events are used because one can use CCQE kinematics to reconstruct the neutrino energy – one can look at neutrino energy spectraWe are looking for an oscillation signal in an EQE distribution of electron eventsOne can use an EQE distribution of muon events to understand our models

measure visible E and from mostly Čerenkov () + some scintillation light (p)

90% purity sampleMain bkgd: CC+

(+ absorbed)

p

nScintillation

Cerenkov 1

12C Cerenkov 2

e

Compare data to the Smith Moniz model implemented

in NUANCE for

CCQE events

n → - p


Deficit is seen in the data for low values of the momentum transfer, Q2

Similar effects have been seen in otherchannels and by other experiments

Given the Fermi gas model approximation used one can imagine deficiencies – particularly in the low Q2 (very forward) kinematic region

Use data sample to adjust available parameters in present model to reproduce data: only – e differences are due to lepton mass effects, vs. e

With the high statistics and resolutions attainable at MiniBooNE, the MiniBooNE data will be used in the future to carefully study this and other models of CCQE interactions

MiniBooNE Quasi-Elastic MiniBooNE Quasi-Elastic DataData

0 0 ’s Background ’s Background DeterminationDetermination

e appearance: 0 production important because background to →e

if ’s highly asymmetric in energy or small opening angle (overlapping rings) can appear much like primary electronemerging from a e QE interaction!

0→Signal N

Reconstruction of Reconstruction of ππ00 results in results in excellent Data/MC agreement.excellent Data/MC agreement.We use Data to reweight (i.e. We use Data to reweight (i.e. tune) NUANCE rate prediction as tune) NUANCE rate prediction as a function of a function of ππ00 momentum. momentum.


We measure rate of We measure rate of ππ00 in the data sample out of the oscillation in the data sample out of the oscillation regionregionand extrapolate it into the oscillation region.and extrapolate it into the oscillation region.

The reconstructed The reconstructed γγγγ mass distribution is mass distribution is divided into 9 momentum divided into 9 momentum bins.bins.

MC is used to unsmear MC is used to unsmear the data:the data:1.1. In bins of true In bins of true

momentum vs. momentum vs. reconstructed reconstructed momentum, count MC momentum, count MC events, over BG, in the events, over BG, in the signal window.signal window.

2.2. Divide by the total Divide by the total number of number of ππ00 events events generated in that true generated in that true momentum bin.momentum bin.

3.3. Invert the matrix.Invert the matrix.

4.4. Perform a BG subtraction Perform a BG subtraction on the data in each on the data in each reconstructed reconstructed momentum bins.momentum bins.

5.5. Multiply the data vector Multiply the data vector by the MC unsmearing by the MC unsmearing MatrixMatrix

Monte Carlo Events Passing Analysis Cuts

All eventsEvents with no

π0

Data Un-smearing and efficiency Data Un-smearing and efficiency correctioncorrection


The Corrected Data DistributionThe Corrected Data Distribution

The corrected The corrected ππ00 momentum distribution is softer momentum distribution is softer than the default Monte Carlo. The normalization than the default Monte Carlo. The normalization discrepancy is across all interaction channels in discrepancy is across all interaction channels in MiniBooNE.MiniBooNE.

From this distribution From this distribution we derive a reweighting we derive a reweighting function for Monte Carlo function for Monte Carlo events. events.

Ratio of Ratio of datadata and and MCMC points points scaled to equal numbers of scaled to equal numbers of events.events.

MC: Generated MC: Generated distribution.distribution.

Data: Corrected to true Data: Corrected to true momentum andmomentum and 100% efficiency.100% efficiency.


Reweighting improves Reweighting improves data/MC agreement.data/MC agreement.

The plots are:The plots are:

• Decay opening angleDecay opening angle

• Energy of high energy Energy of high energy γγ

• Energy of low energy Energy of low energy γγ

• ππ angle wrt the beam angle wrt the beam

The disagreement cos The disagreement cos θθππ may be due to coherent may be due to coherent ππ00 production which we fit production which we fit for.for.

Reweighting MC to DataReweighting MC to Data


The Resulting The Resulting ππ00 MisID MisID DistributionDistribution

The resulting misID The resulting misID distribution is softer in distribution is softer in EEνν QE.QE.

Also there are less Also there are less misID events per misID events per produced produced ππ00 than in the than in the default Monte Carlo.default Monte Carlo.

The error on misID yield The error on misID yield is well below the 10% is well below the 10% target.target.

This is not the final PID cut set!



Cross-ChecksCross-Checks

Important Cross-Important Cross-check… check…

… comes from NuMI events detected in MiniBooNE detector!

MiniBooNE

Decay Pipe

Beam Absorber

We get e , , 0 , +/- , ,etc. eventsfrom NuMI in MiniBooNE detector, allmixed together Use them to Use them to

checkcheck

our our ee reconstruction and PID reconstruction and PID separation!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.

NuMI events cover whole energy region relevant to e osc. analysis at MiniBooNE.

Example of use of the events from NuMI Example of use of the events from NuMI beam beam

Boosted Decision Tree

Likelihood Ratios

e/

e/

PRELIMIN

ARY

PRELIMIN

ARY

PRELIMIN

ARY

PRELIMIN

ARY

Data/MC agree through background and signal regions

PRELIMIN

ARY

PRELIMIN

ARY


Appearance Signal Appearance Signal and Backgroundsand Backgrounds


Arb

itra

ry U

nit

s

Oscillation e

Example (fake) oscillation signal

– m2 = 1 eV2

– sin22 = 0.004

Fit for excess as function of reconstructed e

energy

Appearance Signal and Appearance Signal and BackgroundsBackgrounds

PRELIMIN

ARY

PRELIMIN

ARY


Arb

itra

ry U

nit

sAppearance Signal and Appearance Signal and

BackgroundsBackgroundsMisID • 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

PRELIMIN

ARY

PRELIMIN

ARY


Arb

itra

ry U

nit


BackgroundsBackgroundse from +

• Measured with CCQE sample– Same parent + kinematics

• Most important low E background

• Very highly constrained (a few percent)

PRELIMIN

ARY

PRELIMIN

ARY

p+Be+

e

+

e+


Arb

itra

ry U

nit


BackgroundsBackgrounds

e from K+

• Use High energy e and to normalize

• Use kaon production data for shape

PRELIMIN

ARY

PRELIMIN

ARY


Arb

itra

ry U

nit


BackgroundsBackgrounds

High energy e

data• Events below

1.5 GeV still in closed box (blind analysis)

PRELIMIN

ARY

PRELIMIN

ARY


Combined Fit Combined Fit (Example)(Example)

Combined fit Combined fit constrains constrains uncertainties uncertainties common to common to and e

2==I,J(OI-PI)(CIJ)-1(OJ-PJ)

Systematic error matrixSystematic error matrixCIJ includes estimated includes estimated

systematic uncertaintiessystematic uncertainties

CIJ = e

e e

PRELIMIN

ARY

PRELIMIN

ARY

PI =PI (sin2(2),m2)

Scan Scan sin2(2)e,m2,with

sin2(2)x=0; calculate2 value over ande

bins:bins:


Reconstructed visible Reconstructed visible muon energy (left) muon energy (left) muon neutrino energy muon neutrino energy (right) using CCQE (right) using CCQE data.data.

Error bands show both statistical Error bands show both statistical and systematic errorsand systematic errors

Evaluating Evaluating SystematicsSystematics

PRELIMIN

ARY

PRELIMIN

ARY


LSND best fit sin22 = 0.003 m2 = 1.2 eV2

MiniBooNE Oscillation MiniBooNE Oscillation SensitivitySensitivity

MiniBooNE aims to cover LSND region.

We are currently finalizing work on systematicerror (i.e. error matrix)that combines the error sources(flux, or measured rate, detector

modeling) of signal and the background componentsto predict sensitivity to oscillation signal


Total accumulated dataset 7.5 x 1020 POT, world’s largest dataset in this energy range.

Jan 2006: Started running with antineutrinos.

Detected NuMI neutrinos – using in analysis.

Oscillation Analysis progress: we are preparing

to open the closed “oscillation box”.

SummarySummary


Backup SlidesBackup Slides


– Sterile Neutrinos • RH neutrinos that don’t interact (Weak == LH only)

– CPT Violation• 3 neutrino model, manti-

2 > m2

• 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

Explaining the LSND Explaining the LSND resultresult


World p+Be MeasurementsWorld 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

kinematic boundary of HARP measurement at exactly 8.9 GeV/c

● black boxes are the distribution of + which decay to a that passes through the MiniBooNE detector

HARP ResultsHARP Results

HARP (CERN)Data taken with MiniBooNE target slugs using 8 GeV beamResults on thin target just added (Apr06).

Further improvement in flux prediction expected soon with HARP thick target and K data


Exclusive channels are handled separately and use differing, appropriate models

Total cross-sections are then the sum of all relevant exclusive channels

Nuclear effects of hadrons propagating through the nucleus are considered to give you an expected final state condition

The most critical exclusive channel for the MiniBooNE oscillation search is the charged-current quasi-elastic interaction

NUANCE models CCQE events using the relativistic Fermi gas model of Smith and Moniz as a framework

The next most critical exclusive channels are the NC production of NC 0's

NUANCE uses the resonant and coherent p0 production models of Rein and Sehgal

About NUANCEAbout NUANCE


• NuMI ’s sprayed in all directions.• K and decays at off-axis

angle:

p beam , K

22

,

2

2,

2

1

1

K

K

EM

m

E

• Opportunity to check the /K ratio of yields off the target.

~110mrad to MiniBooNE

NuMI events at NuMI events at MiniBooNE MiniBooNE


Production of the Production of the 0 0 ’s’s

Resonant 0 production

N N=(p,n)

0 N’

Coherent 0 production

A A 0 0→

In addition to its primary decay In addition to its primary decay NN, , the the resonance has a branching resonance has a branching fraction fraction of 0.56% to of 0.56% to NN final state. final state.

e appearance: 0 production important because background to →e

if ’s highly asymmetric in energy or small opening angle (overlapping rings) can appear much like primary electronemerging from a e QE interaction!

0→Signal N


ReweightedReweighted

UnweighteUnweightedd

The fit coherent fraction The fit coherent fraction is higher after is higher after reweighting.reweighting.

This was expected based This was expected based on the additional peaking on the additional peaking in the reweighted cos in the reweighted cos θθ distribution.distribution.

The reweighted fit does The reweighted fit does much better in the much better in the important forward region.important forward region.

Fit coherent, resonant, and background components to the Fit coherent, resonant, and background components to the datadata

Coherent Fit EffectCoherent Fit Effect

Zelimir Djurcic Physics Department Columbia University Status of MiniBooNE Status of...

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