neutrino oscillation results from minos and miniboone

Neutrino Oscillation Results from MINOS and MiniBooNE

Tobias RauferRutherford Appleton

Laboratory

for the MINOS collaboration

FPCP08, Taipei, 5-9 May, 2008

Introduction to Neutrino Oscillations• Neutrino masses and mixing• Sterile neutrinos • Current world knowledge on neutrino oscillation parameters

Results from MINOS• The NuMI beam and MINOS detectors• Charged-current disappearance analysis• Neutral current analysis

• νe appearance status

Results from MiniBooNE• The MiniBooNE Beam and Detector• Results

Outlook

Overview

2

Introduction to Neutrino Oscillations

Neutrinos mix

Two conditions necessary for neutrino oscillations:– Neutrinos mix– Neutrinos are massive

Flavour eigenstates

Mass eigenstates

flavour eigenstates govern interactions

mass eigenstates propagate

4

Talk by Reyco Henning

Atmospheric+LBL Chooz Solar+KamLAND Majorana

Neutrinos are massive

There are only 3 light neutrinos coupling to the Z0 …

5

Neutrinos are massive

There are only 3 light neutrinos coupling to the Z0 …

6

Neutrinos are massive

… but there could be sterile neutrinos!

There are only 3 light neutrinos coupling to the Z0 …

7

Neutrinos oscillate

If mass and weak eigenstates are different:

– Neutrino is produced in weak eigenstate.

– It travels a distance L as a (superposition of) mass

eigenstate(s).

– It is detected as a (possibly different) weak eigenstate.

8

Neutrino oscillations: disappearance

• Experiments in Homestake, Kamioka

and Sudbury established deficit of νe from

the sun

• Total solar flux measured by SNO

agrees with prediction

• Super-K, K2K and MINOS have

measured L/E form of νμ disappearance

rate

• KamLAND have measured L/E form of

reactor anti-νe disapperance rate

•CHOOZ have placed a limit on θ13 using

non-disappearance of reactor anti-νe

arXiv:0801.4589 [hep-ex]

9

Super-K Zenith Angle

Upward stopping Sub GeV Multi ring ()

Multi GeV Multi ring ()

Upward through going

Honda Best fitSub GeV 1ring e-like

Sub GeV 1ring -like

Multi GeV 1ring e-like

Multi-GeV 1ring -like + Partially Contained

Reactor Experiments

νe

νe

νe

νe

νe

νe

Distance

Pro

babi

lity

νe

1.0

Well understood, isotropic Well understood, isotropic source of electron anti-source of electron anti-neutrinosneutrinos

Oscillations observed as Oscillations observed as disappearance of disappearance of ννee

sinsin2222θθ

1313

Survival ProbabilitySurvival Probability

+ O(m122 / m13

2)

P(e e)1 sin2 213 sin2(1.27m13

2 L /E )

Neutrino oscillations: appearance

• Another signature of neutrino oscillation is appearance of ''wrong'' flavour neutrinos• LNSD observes excess ofanti-νe

in anti-νμ beam:

87.9 ± 22.4 ± 6.0 (3.8σ)

LSND signal

12

Neutrino oscillation parameters

MINOS result later in the talk!

13

The MINOS experiment

The MINOS experiment

MINOS (Main Injector Neutrino Oscillation Search)– Long-baseline neutrino

oscillation experiment– Neutrino beam provided by 120

GeV protons from the Fermilab Main Injector

Basic concept– Measure energy spectrum at the

Near Detector, at Fermilab– Measure energy spectrum at the

Far Detector, 735 km away, deep underground in the Soudan Mine

– Compare Near and Far measurements to study neutrino oscillations 15

Producing Neutrinos

• Neutrinos from the Main Injector (NuMI)

• 10 μs spill of 120 GeV protons every 2.4 s

• 180 kW typical beam power

• 2.5 1013 protons per pulse

• Neutrino spectrum changes

with target position

Tobias Raufer 1616

MINOS Detectors

Detectors magnetised to ~1.3 T

GPS time-stamping to synch FD data to ND/Beam

Flexible software triggering in DAQ PCs: FD triggers from FNAL over IP

Coil

Veto Shield Far

Near

Plane installation fully completed on Aug 11, 2004

5.4 kt mass, 8830m 484 steel/scintillator planes Divided into 2 super modules

M64 multi-anode PMTs

1 kt mass, 3.84.815m282 steel and 153 scintillator

planesFront 120 planes Calorimeter

Remaining planes SpectrometerM16 multi-anode PMTs

Tobias Raufer 1717

νμ CC Event νe CC EventNC Eventνμ CC Event NC Event νe CC Event

Event Topologies

18

νμ CC Event νe CC EventNC Eventνμ CC EventUZ

VZ

long μ track & hadronic activity at vertex

3.5m

Monte CarloNC Event νe CC Event

Event Topologies

19

νμ CC Event νe CC EventNC Eventνμ CC EventUZ

VZ

long μ track & hadronic activity at vertex

3.5m

Monte CarloNC Event

short event, often diffuse

1.8m

νe CC Event

Event Topologies

20

νμ CC Event νe CC EventNC Eventνμ CC EventUZ

VZ

long μ track & hadronic activity at vertex

3.5m

Monte CarloNC Event

short event, often diffuse

1.8m

νe CC Event

short, with typical EM shower profile

2.3m

Event Topologies

21

Event Topologiesνμ CC Event νe CC EventNC Eventνμ CC Event

UZ

VZ

long μ track & hadronic activity at vertex

3.5m

Monte CarloNC Event

short event, often diffuse

1.8m

νe CC Event

short, with typical EM shower profile

2.3m

Energy resolution

π±: 55%/E(GeV)

μ±: 6% range, 10% curvature

22

CC disappearance measurement

CC disappearance

Selecting charged-current events:• Reconstructed track with θ<53° w.r.t. beam direction

• Vertex in fiducial volume

• In time with beam spill

• Reject NC background using a likelihood ratio discriminant

constructed from 6 variables, e.g.

24

LE-10/170kA LE-10/185kA

pME/200kA

Horn off

LE-10/200kA

pHE/200kA

Hadron Production Tuning

Use tuning of hadron production in CC events to provide flux corrections for Monte Carlo

Parameterize Fluka2005 prediction as a function of xF and pT

Perform fit which reweights neutrino parent pion xF and pT to improve data/MC agreement

25

Hadron production in the NuMI target has large uncertainties! uncertain beam flux

• directly use Near Detector data to perform extrapolation between Near and Far

• use Monte Carlo to provide necessary corrections due to energy smearing and acceptance.

• use our knowledge of pion decay kinematics and the geometry of our beamline to predict the FD energy spectrum from the measured ND spectrum

Predicting the FD spectrum

FD

Decay Pipe

π+Target

ND

p

26

CC disappearance Result

m322 2.38 0.16

0.20(stat syst) 10 3eV2

sin2(223) 1.00 0.08(stat syst)

27

NC measurement

Neutral Current Analysis

Why look at NC events?• If oscillations only involve active neutrinos, NC events

are unaffected.• Oscillations into sterile neutrinos cause energy

dependent deficit in neutral current energy spectrum.

Toy Simulation

No νs

With νs mixing

29

NC Event pre-selection

• FD: remove cosmics, detector noise and split events– Fiducial volume and cleaning cuts

• ND: many interactions in one beam spill, both inside the detector and in the surrounding rock– Separate events based on topology and timing– Tight fiducial volume

Calorimeter Spectrometer

30

NC Event selection

• Event classified as NC-like if:

− event length < 60 planes

− has no reconstructed track or

− has one reconstructed track that does not protrude more than 5 planes beyond the shower

• Final neutral current event selection proceeds via cuts on three variables• Error envelopes shown reflect systematic uncertainties due to cross-section modeling

and beam modeling

Excluded Excluded

Excluded

31

NC Energy Spectrum

• Far Detector reconstructed energy spectrum for NC-like events.

• Oscillation parameters are fixed. MC predictions with Θ13=0 and Θ13 at the CHOOZ limit are shown.

32

Is there a deficit?

• Comparisons between observed Data and MC Prediction

• Significance is given by

• For the 0-3 GeV reconstructed energy range, a 1.15σ

difference between Data and Monte Carlo is observed in the case where Θ13 = 0.

33

4-flavour Model

• Introduce one additional, sterile neutrino

• Assume Δm241= 0

• Oscillation at single mass scale • Oscillation probabilities simplify to:

• Fit for Δm231, |Uμ3|2 and |Us3|2

• Joint fit of NC and CC spectra

• Fix |Ue3|2 = 0 and 0.04 (CHOOZ limit) 34

4-flavour Result

• 90% C.L. contour for the fit to |Us3|2 and |Uμ3|2

• Showing the limiting cases: |Ue3|2=0 and |Ue3|2=0.04

35

Outlook

• 90% C.L. sensitivity curves for different NuMI beam exposures

• Input values of oscillation parameters– |U3|2 = 0.5, |Us3|2 = 0.1, Δm2

32 = 2.38 x 10-3 eV2,|Ue3|2 = 0

• Only MC events are used

36

νe appearance search

MINOS νe appearance search

• Search for far detector νe appearance in initially 99% νμ beam• Select νe with neural net based algorithm• Selected near detector events are mostly CCνμ and NC• Selection depends on details of hadronic simulation• Solution: use two independent data driven methods to estimate NC and CCνμ backgrounds

selected νe sample

38

MINOS νe sensitivity

• Projected limits shown with current and expected MINOS exposure

• At CHOOZ limit expect 12 νe signal events and 42 background events with 3.25x1020 protons

• Use sidebands to study predicted far detector backgrounds

• Expect first result later this year

39

The MiniBooNE experiment

MiniBooNE searches for νμ→νe

• 8GeV/c protons hit beryllium target

• 4x1012 protons/spill with up to 4Hz rate

• 174kA pulsed magnetic horn focuses positively

charged hadrons: x6 flux gain

• Detector is 800 tons of mineral oil placed with L/E

similar to LSND:

LSND: 0.03km/0.05GeV ~ 0.60 km/GeV

MiniBooNE: 0.50km/0.80GeV ~ 0.63 km/GeV

41

MiniBooNE flux prediction

• HARP pion data were fit with

Sanford-Wang parametrization:

~17% uncertainty

• Fit world kaon data in 10-24GeV

using Feynman scaling:

~30% uncertainty

• Kaon flux is checked with off-axis

''Little Muon Counter'' and high

energy events

• Geant4 simulation of beamline:

target, horn, decay volume and

absorber

K

e e

K e e

e/= 0.5%

42

veto cutremoves cosmic μ

CC1π3 subevents

CCQE2 subevents

• PMTs collect scintillation and Cherenkov light

• Subvent is set of PMT hits close in time

• Select subevents within beam spill window

• Number of veto hits < 6

• Number of tank hits > 200

• Fiducial R < 500cm

• Only 1 subevent in spill window -

removes CC1π and CCQE events

MiniBooNE beam eventsveto hits<6 veto hits<6

tank hits>200

removes electrons from cosmic μdecays

43

MiniBooNE νe selection: step 1

Signal

Background

Background

,ΦE

t,x,y,z

light

• Maximize likelihood of observed hits under 2 hypotheses: 1) track is an electron 2) track is a muon• Vary 7 parameters

Select electron tracks using likelihood ratio

44

MiniBooNE νe selection: step 2

• Maximize likelihood of observed hits under 2 hypotheses with 10 parameters: 1) event is electron 2) event is π0→γγ

• Select electron events using likelihood ratios

E1,1,Φ1

t,x,y,z

lights1

s2E2,2Φ2

blin

d

π0 mass≈135Mev

45

MiniBooNE νe signal prediction

• Blind analysis: signal box was opened in steps by gradually revealing more details

about data events

• Event selection tuning and background estimation used events outside signal

region

• ''Bad'' initial χ2 for data and predicted visible energy (null and best fit): move energy

threshold to 475MeV

46

• No excess of νe events in expected

signal region from LSND

• Significance (stat + syst error):475-1250 MeV: 22 ± 40300- 475 MeV: 95 ± 28200- 300 MeV: 91 ± 31

What does it all mean?

LSND was wrong? Difference between νe and anti-νe? New physics that doesn't scale with

L/E? Detector or flux effects?

MiniBooNE Result

47

Summary & Outlook• MINOS

– MINOS is steadily accumulating data– CC disappearance result for 2.5 x 1020 POT:

– NC result for 2.5 x 1020 POT:• 3-flavour analysis: 1.15σ deficit for E < 3GeV

consistent with no sterile admixture

• 4-flavour analysis:

In the near future:– Updated CC result with more data and an improved analysis– First MINOS electron-neutrino appearance result

• MiniBooNE– No excess observed – Incompatible with LSND at 98% C.L. (two-flavour approx.)

m322 2.38 0.16

0.20(stat syst) 10 3eV2, sin2(223) 1.00 0.08(stat syst)

Us3

2 0.14 0.130.18, for Ue3

2 0 (no e admixture)

Us3

2 0.21 0.120.20, for Ue3

2 0.04 ( e admixture)

48

Back-up slides

CC future sensitivity

50

Accumulated Beam Data

RUN I - 1.24x1020 POT (LE)(PRL Publication

disappearance)

Higher energy beam

RUN IIa1.22x1020 (LE) POT(new)

RUN IIb~0.75x1020 POT(not included)This Analysis: Run I + Run IIa => 2.46x1020 POT

(LE Beam only)

RUN III>0.9x1020 POT(current)

51

Predicting the FD spectrum

x =

- Beam Matrix

- F/N

- NDFit

- 2DFit

52

This method is known as the Beam Matrix method.

Far/Near Ratio Method• An approach that uses the ND data in a non-parameterized way is

provided by the F/N ratio method:

• For every event that passes FD NC selection, a reconstructed energy vs true energy 2D histogram is created– Oscillation weights are calculated for bins of true Energy

– For each bin of true energy, the reconstructed energy projection is multiplied by the corresponding oscillation weight

– Prediction is obtained by multiplying each bin by NiData/Ni

MC

• Simple, makes no assumptions about ND Data parameterization, robust to systematic errors

FDipredicted

FDiMC

NDiMCNDi

Data

53/5

• Normalization: 4% – POT counting, Near/Far reconstruction efficiency, fiducial mass

• Relative Hadronic Calibration: 3%– Inter-Detector calibration uncertainty

• Absolute Hadronic Calibration: 11%– Hadronic Shower Energy Scale(6%), Intranuclear rescattering(10%)

• Muon energy scale: 2%– Uncertainty in dE/dX in MC

• CC Contamination of NC-like sample: 15%• NC contamination of CC-like sample: 25%• Cross-section uncertainties:

– mA (qe) and mA (res): 15%– KNO scaling: 33%

• Poorly reconstructed events: 10% • Near Detector NC Selection: 8% in 0-1 GeV bin• Far Detector NC Selection: 4% if E < 1 GeV, <1.6% if E > 1 GeV • Beam uncertainty: 1 error band around beam fit results

Systematic Errors

54

• Systematic errors studied using simulated Far Detector data histograms with oscillation parameters m2 = 2.38 x10-3 eV2, sin2223=1

• Left plot displays magnitude of shift in FD simulated data compared to nominal• Ratio plots show shifted/nominal ratio for FD simulated data, overlaid with

shifted/nominal MC FD prediction – Displays ability of F/N extrapolation method to reproduce systematic shift

• relative, where shifts only applied to one detector

Systematic Errors

Simulated Data

55

Systematic Errors

56

MiniBooNE cross-section model

• Nuance is tuned to world ν data

• Same approach for Neugen/MINOS

• Monte-Carlo event rates are

adjusted using MiniBooNE data

outside signal region

Nuance ν generatorD. CasperNPS 112 (2002) 161

PRL 100, 032301 (2008).

arXiv:0803.3423 [hep-ex]

57