NOvA Muon Neutrino and Antineutrino Disappearance Results 2018 - - PowerPoint PPT Presentation

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NOvA Muon Neutrino and Antineutrino Disappearance Results 2018

Dmitrii Torbunov

University of Minnesota

June 19, 2018

FERMILAB-SLIDES-18-091-LBNF-ND This document was prepared by [NOvA Collaboration] using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User

• Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No.

DE-AC02-07CH11359.

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Introduction

◮ NOvA uses NuMI muon (anti)neutrino beam. Thank you

Fermilab for this tremendous beam.

◮ The muon beam travels from the Near Detector at Fermilab

to the Far Detector in northern Minnesota.

◮ As muons travel, some fraction of νµ oscillates into νe and ντ. ◮ The muon neutrino disappearance analysis aims to determine

(θ23, ∆m2

32) oscillation parameters, by measuring the number

• f survived muon neutrinos at the Far Detector.

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Numu Analysis, Idea

◮ Muon neutrino oscillations

produce a dip between 1-2 GeV range.

◮ Dip depth depends on the

mixing angle θ23.

◮ Precise position of the dip

depends on the ∆m2

32. ◮ But, presence of systematic

uncertainties requires extra work to minimize their influence.

Reconstructed Neutrino Energy (GeV)

1 2 3 4 5

Events / 0.1 GeV

20 40 60

Δm 2 sin22θ23

no oscillations with oscillations

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Numu Analysis 1, How to detect a muon neutrino?

◮ Muon neutrinos do not have electric charge, so they do not

leave any tracks.

◮ We can only detect neutrinos if they happen to interact with

the detector.

◮ The only such reaction, that allows us to identify a muon

neutrino is a charged current interaction:

Muon Proton νμ + n → μ + p

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Numu Analysis 2, Muon Neutrino selection 1

NOvA - FNAL E929 Run: 18791 / 48 Event: 765587 / -- UTC Fri Jan 30, 2015 07:19:18.516289184 100 200 300 400 500

sec) µ t (

1 10

2

10

3

10

hits

10

2

10

3

10

q (ADC)

1 10

2

10

3

10

4

10

hits

1000 2000 3000 4000 5000 6000 500 − 500

x (cm)

1000 2000 3000 4000 5000 6000

z (cm)

500 − 500

y (cm)

Where is a muon neutrino here?

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Numu Analysis 2, Muon Neutrino selection 2

◮ We identify the νµ CC event by the presence of the muon. ◮ First, we have a great computer vision algorithm CVN, which

can identify particle types.

◮ But, we must also use another algorithm which looks at the

physical properties of the tracks to distinguish muon tracks from pion tracks (which look similar).

◮ Finally, there are a lot of muons coming from the top –

• cosmics. To minimize cosmics background we:

◮ use small timing window around the beam pulse ◮ veto muons that come from the edge of the detector ◮ have an algorithm that rejects the remaining muons, by

analyzing their kinematics

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Numu Analysis 2, Muon Neutrino selection 3

NOvA - FNAL E929 Run: 18791 / 48 Event: 765587 / -- UTC Fri Jan 30, 2015 07:19:18.516289184 100 200 300 400 500

sec) µ t (

1 10

2

10

3

10

hits

10

2

10

3

10

q (ADC)

1 10

2

10

3

10

4

10

hits

1000 2000 3000 4000 5000 6000 500 − 500

x (cm)

1000 2000 3000 4000 5000 6000

z (cm)

500 − 500

y (cm)

Here is a muon neutrino!

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Numu Analysis 3, Energy reconstruction

◮ Since we are looking at the νµ CC events of the form:

Muon Proton νμ + n → μ + p

◮ The energy of the muon neutrino is

Eνµ = Eµ + Ehad

◮ Energy of the muon Eµ is reconstructed from the muon’s

track length.

◮ Hadronic energy Ehad is reconstructed from the visible

calorimetric energy.

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Numu Analysis 4, Quartiles split

◮ After, we have reconstructed neutrino’s energies, we split our

sample into 4 classes(also known at NOvA as ”quartiles”)

◮ The split is based on the fraction energy in the event, that

comes from hadronic activity(i.e. not from muon)

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Quartile boundaries for muon (anti)neutrinos

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Numu Analysis 5, Exptrapolation

Finally, we use the Near Detector data to construct the Far Detector predicted energy spectrum:

1 2 3 4 5 2 4 6 8 20 40 60 80 1 2 3 4 5 1 2 3 4 1 2 3 4 5 1 2 3 4 1 12 2 0 1

ND Events/1 GeV

5

10 True Energy (GeV) True Energy (GeV) ND Reco Energy (GeV) FD Reco Energy (GeV) FD Events/1 GeV ND Events

5

10 FD Events F/N Ratio

• 3

10 )

µ

ν →

µ

ν P(

ND data Base Simulation Data-Driven Prediction

This extrapolation procedure helps us to minimize flux and cross-section uncertainties.

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Numu Analysis 6, Data/Prediction comparison, Details

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8 10 12

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Neutrino beam NOvA Preliminary All Quartiles

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Antineutrino beam NOvA Preliminary All Quartiles

Beam Neutrinos Antineutrinos Exposure 8.9 · 1020 POT 6.9 · 1020 POT Total Observed 113 65 Best fit prediction 121 50 Cosmic Bkgd. 2.1 0.5 Beam Bkgd. 1.2 0.6 Unoscillated 730 266 Prediction here is based on the best fit values for joint numu-disappearance and nue-appearance analysis

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Comparison with other experiments

0.4 0.5 0.6

23

θ

2

sin

2.0 2.5 3.0

)

2

eV

• 3

(10

32 2

m ∆

Best fit

NOvA Preliminary Normal Hierarchy 90% CL NOvA MINOS 2014 T2K 2017 IceCube 2017 SK 2017

Best fit: sin2 θ23 = 0.58 ± 0.03, ∆m2

32 = 2.51+0.12 −0.08 · 10−3eV2

Combined muon neutrino disappearance and electron neutrino appearance results

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Conclusions

◮ We have the first results with neutrino and antineutrino data

(sin2 θ23 = 0.58 ± 0.03, ∆m2

32 = 2.51+0.12 −0.08 · 10−3eV2). ◮ In the future we expect to refine our measurements of θ23 and

∆m2

32 by accumulating higher exposure values. ◮ This will improve NOvA’s sensitivity to the mass hierarchy,

θ23 octant and CP violation.

◮ Please find the full NOvA νµ disappearance and νe

appearance results in the next talk.

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Backups

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Data/Prediction comparison, Neutrinos

◮ We have observed 113 muon

neutrino like events: Total Observed 113 Best fit prediction 121 Cosmic Bkgd. 2.1 Beam Bkgd. 1.2 Unoscillated 730

◮ Corresponding exposure 8.9 · 1020

POT.

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8 10 12

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Neutrino beam NOvA Preliminary All Quartiles

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Data/Prediction comparison, Antineutrinos

◮ We have observed 65 muon

antineutrino like events: Total Observed 65 Best fit prediction 50 Cosmic Bkgd. 0.5 Beam Bkgd. 0.6 Unoscillated 266

◮ Corresponding exposure 6.9 · 1020

POT.

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Antineutrino beam NOvA Preliminary All Quartiles

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Numu Contours

Below are the contours that show 90% confidence contours for the

• scillation parameters θ23, ∆m2

32

0.3 0.4 0.5 0.6 0.7

23

θ

2

sin

2.2 2.4 2.6 2.8 3.0

)

2

eV

• 3

(10

32 2

m ∆

NOvA NH 90% CL

• nly

µ

ν

µ

ν +

µ

ν

• nly

µ

ν

µ

ν 2017 No Feldman-Cousins NOvA Preliminary

Consistency with the combined fit oscillation parameters for the neutrino and antineutrino datasets is better than 4%.

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Octant-Hierarchy Sensitivity

Joint νµ + ¯ νµ fit prefers higher octant of θ23 for the normal hierarchy and lower octant for the inverted hierarchy.

0.3 0.4 0.5 0.6 0.7

23

θ

2

sin

2 4 6 8

)

2

χ ∆ Significance (

• nly

µ

ν

µ

ν +

µ

ν

• nly

µ

ν NOvA NH NOvA IH No Feldman-Cousins NOvA Preliminary

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Numu Analysis 4, Motivation for Quartile split

Why do we split our sample in 4 parts?

◮ The NOvA’s sensitivity towards oscillation parameters

depends on how well we reconstruct neutrino energy.

◮ It turns out, that we can reconstruct muon’s energy much

better than the hadronic energy.

◮ So, by separating sample into 4 quartiles we can better exploit

good energy reconstruction of the quartiles with low fraction

• f hadronic energy.

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Numu Analysis 4, Motivation for Quartile split 2

POT / 0.1 GeV

20

10 × Events / 3.10

3

10 POT / 0.1 GeV

20

10 × Events / 3.10

3

10 POT / 0.1 GeV

20

10 × Events / 3.10

3

10 POT / 0.1 GeV

20

10 × Events / 8.03

3

10 POT / 0.1 GeV

20

10 × Events / 8.03

3

10 POT / 0.1 GeV

20

10 × Events / 8.03

3

10

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

5 10 15

POT / 0.1 GeV

20

10 × Events / 3.10

3

10

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8 10

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

20 40 60

POT / 0.1 GeV

20

10 × Events / 8.03

3

10

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

10 20 30 40 50

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

10 20 30

NOvA Preliminary

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

10 20 30 40 50

NOvA Preliminary

Neutrinos Antineutrinos

Data Simulation

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total Background Area Normalised

Q1 Q2 Q3 Q4

Best resolution: ~6% Worst resolution: ~12%

Near Detector Data and Simulation comparison. All points are within 1 − 10%.

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Numu Analysis 6, Data/Prediction comparison, Details

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8 10 12

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Neutrino beam NOvA Preliminary All Quartiles

1 2 3 4 5

Reconstructed Neutrino Energy (GeV)

2 4 6 8

Events / 0.1 GeV

FD Data Prediction

• syst. range

σ 1- CC

µ

ν Wrong Sign: Total bkg. Cosmic bkg.

Antineutrino beam NOvA Preliminary All Quartiles

Beam Neutrinos Antineutrinos Exposure 8.9 · 1020 POT 6.9 · 1020 POT Total Observed 113 65 Best fit prediction 121 50 Cosmic Bkgd. 2.1 0.5 Beam Bkgd. 1.2 0.6 Unoscillated 730 266