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NOvA (with focus on the mass hierarchy) Jeff Hartnell University - - PowerPoint PPT Presentation

Nov 23, 2022 338 likes •847 views

NOvA (with focus on the mass hierarchy) Jeff Hartnell University of Sussex Solvay Workshop, Brussels 30 th November 2017 IntroducAon NOvA experiment and physics goals NuMI beam NOvA detectors Mass hierarchy via MSW maHer effect

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SLIDE 1

NOvA

(with focus on the mass hierarchy)

Jeff Hartnell

University of Sussex Solvay Workshop, Brussels 30th November 2017

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SLIDE 2

IntroducAon

  • NOvA experiment and physics goals

– NuMI beam – NOvA detectors

  • Mass hierarchy via MSW maHer effect
  • Nue and nuebar appearance probabiliAes
  • Results:

– Muon neutrino disappearance – NC analysis – Electron neutrino appearance

  • Future sensiAvity

Jeff Hartnell, Solvay 2017 2

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SLIDE 3

Jeff Hartnell, Solvay 2017 3

NOvA Overview

  • “ConvenAonal” beam
  • Two-detector experiment:
  • Near detector

– measure beam composiAon – energy spectrum

  • Far detector

– measure oscillaAons and search for new physics

Ash River

Ash River

810 km

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SLIDE 4

The NOvA CollaboraAon

242 Collaborators 49 institutions 7 countries

Argonne, AtlanAco, Banaras Hindu University, Caltech, Cochin, InsAtute of Physics and Computer science of the Czech Academy of Sciences, Charles University, CincinnaA, Colorado State, Czech Technical University, Delhi, JINR, Fermilab, Goiás, IIT GuwahaA, Harvard, IIT Hyderabad, U. Hyderabad, Indiana, Iowa State, Jammu, Lebedev, Michigan State, Minnesota-Twin CiAes, Minnesota-Duluth, INR Moscow, Panjab, South Carolina, SD School

  • f Mines, SMU, Stanford, Sussex, Tennessee, Texas-AusAn, Tu[s,

UCL,Virginia, Wichita State, William and Mary, Winona State

4 Jeff Hartnell, Solvay 2017

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SLIDE 5

Physics Goals

Jeff Hartnell, Solvay 2017 5

Results from 3 different oscillation analyses

¨ Disappearance of

νµ CC events

¤ clear suppression as a

funcAon of energy

¤ 2016 analysis results

PRL 118.151802

sin2(2θ23)

  • ∆m2

32

  • ¨ Appearance of νe CC

events

¤ 2 GeV neutrinos

enhances maHer effects

¤ ±30% effect

¤ 2016 analysis results

in PRL 118.231801.

θ13, θ23, δCP , and Mass Hierarchy

∆m2

41, θ34, θ24

¨ Deficit of NC events?

¤ suppression of NCs could be evidence

  • f oscillaAons involving a sterile

neutrino

¤ Fit to 3+1model ¤ new!

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SLIDE 6

Jeff Hartnell, Solvay 2017 6

Off-axis

On-axis

NuMI Beam

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SLIDE 7

Jeff Hartnell, Solvay 2017 7

To APD 4 cm ⨯ 6 cm 1560 cm

A NO𝜉A cell

NO𝜉A detectors

Fiber pairs from 32 cells 32-pixel APD

Far detector:

14-kton, fine-grained, low-Z, highly-active tracking calorimeter → 344,000 channels

Near detector:

0.3-kton version of the same → 20,000 channels Extruded PVC cells filled with 11M liters of scintillator instrumented with 𝜇-shifting fiber and APDs

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SLIDE 8

Jeff Hartnell, Solvay 2017 8

Long-baseline neutrino oscillations

𝜉𝜈 disappearance:

…to leading order

experimental data are consistent with unity (“maximal mixing”) Need a leap in precision on 𝜄23 (and m2 )

32

𝜉e appearance:

Daya Bay reactor experiment: sin2(2𝜄13) = 0.084 ± 0.005

…plus potentially large CPv and matter effect modifications!

Non-zero 𝜄 opens the long-baseline appearance channel, and…

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SLIDE 9

Jeff Hartnell, Solvay 2017 9

StarAng with νμ

νµ ντ νe

L/E (km/GeV) 0.2 0.4 0.6 0.8 1

Oscillation Probability

1000 2000

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SLIDE 10

How does the mass hierarchy come into play?

Jeff Hartnell, Solvay 2017 10

Δm2

31 and Δm2 32 differ by 3%

Small effect JUNO’s planned measurement involves this

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SLIDE 11

MaHer Effect & Mass Hierarchy

  • Neutrinos (and anAneutrinos) travel through

maHer not anAmaHer

– electron density causes asymmetry (fake CPv!)

  • via specifically CC coherent forward elasAc scaHering

– different Feynman diagrams for νe and νe interacAons with electrons so different amplitudes

Jeff Hartnell, Solvay 2017 11

Arrows flip for antineutrinos

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SLIDE 12

Jeff Hartnell, Solvay 2017 12

Long-baseline 𝜉𝜈→𝜉e

For fixed L/E = 0.4 km/MeV

A more quantitative sketch… At right: P(𝜉 ⎺𝜈→ 𝜉 ⎺e) vs. P(𝜉𝜈→𝜉e) plotted for a single neutrino energy and baseline

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SLIDE 13

Jeff Hartnell, Solvay 2017 13

Long-baseline 𝜉𝜈→𝜉e

For fixed L/E = 0.4 km/MeV

A more quantitative sketch… At right: P(𝜉 ⎺𝜈→ 𝜉 ⎺e) vs. P(𝜉𝜈→𝜉e) plotted for a single neutrino energy and baseline Measure these probabilities (an example measurement

  • f each shown)

Also: Both probabilities ∝ sin2𝜄23

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SLIDE 14

Jeff Hartnell, Solvay 2017 14

Non-maximal mixing scenario

  • If θ23 non-maximal

then effect of octant is important

  • Big effect, +/- 20%

inverted% hierarchy normal% hierarchy Θ23%<%45o Θ23%>%45o

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SLIDE 15

Jeff Hartnell, Solvay 2017 15

Effect of Increasing Energy

1 2 3 4 5 6 7 8

<P(!µ !e)> [%]

1 2 3 4 5 6 7 8

<P(!µ !e)> [%]

Normal Mass Hierarchy Inverted Mass Hierarchy " = 0 " = #/2 " = # " = 3#/2 L = 1300 km, <E> = 3.2 GeV sin 22$13 = 0.09

𝜉𝜈→𝜉

For fixed L/E = 0.4 km/MeV

A more quantitative sketch… 𝜉 ⎺𝜈→𝜉 ⎺ 𝜉𝜈→𝜉 ) ino

Increasing Energy 0.6 GeV 2 GeV 3 GeV T2K NOvA DUNE

[à bigger matter effect and hence bigger fake CP violation]

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SLIDE 16

The measurements

Jeff Hartnell, Solvay 2017 16

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SLIDE 17 q (ADC) 10 102 3 10

νμ

e

νe

p μ p

1m 1m

ν

νµ CC νe CC NC

~5m ~2.5m

Long, straight track Shorter, wider, fuzzy shower Diffuse activity from nuclear recoil system

Event Types

Jeff Hartnell, Solvay 2017 17

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SLIDE 18

Jeff Hartnell, Solvay 2017 18

𝜉𝜈 disappearance

(simulated 𝜉𝜈 CC event)

  • Identify contained 𝜉𝜈 CC events in each detector
  • Measure their energies
  • Extract oscillation information from differences between

the Far and Near energy spectra

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SLIDE 19

νμ Near Detector Data

Jeff Hartnell, Solvay 2017 19

νμ

–

μ ν

μ ν

–

μ

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SLIDE 20

νμ Far Detector Data

Jeff Hartnell, Solvay 2017 20

Reconstructed neutrino energy (GeV)

1 2 3 4 5

Events / 0.25 GeV

5 10 15 20

Prediction, no systs.

  • syst. range

σ 1- Prediction with systs. Backgrounds Data Normal Hierarchy

NOvA Preliminary

78 events observed in FD

– 473±30 with no oscilla5on – 82 at best oscillaAon fit – 3.9 beam BG + 2.7 cosmic

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SLIDE 21

νμ Disappearance Result

Jeff Hartnell, Solvay 2017 21

23

θ

2

sin

0.3 0.4 0.5 0.6 0.7

)

2

eV

  • 3

(10

32 2

m ∆

2 2.5 3 3.5

NOvA Preliminary

Normal Hierarchy, 90% CL NOvA 2016 T2K 2014 MINOS 2014

Best Fit (in NH):

  • ∆m2

32

  • = 2.67 ± 0.12 × 10−3eV2

sin2 θ23 = 0.40+0.03

−0.02(0.63+0.02 −0.03) No FC Correction

Maximal mixing excluded at 2.6σ

Driven by bins in oscillation dip (1-2 GeV). Forcing maximal mixing gives:

∆m2

32 = (2.46) × 10−3eV2

νμ

–
  • ts,
  • νμ
  • m
  • 𝜾𝟑𝟒

𝜠𝒏𝟒𝟑

𝟑

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SLIDE 22

Neutral Current Result

(NOvA’s first 2017 dataset result, presented at NuFact Sep/17)

Jeff Hartnell, Solvay 2017 22

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SLIDE 23

NC Far Detector Data & Results

Jeff Hartnell, Solvay 2017 23 –

Observed 214 NC candidates Prediction 191.16 ± 13.82(stat.)±21.99 (syst.) No depletion of NC events observed NOvA sees no evidence for νs mixing

– θ

𝟐. 𝟐𝟘𝟏 ± 0.160 (𝑡𝑢𝑏𝑢. )−0.130

+0.080 (𝑡𝑧𝑡𝑢. )

θ

𝟐. 𝟐𝟘𝟏 ± 0.123 (𝑡𝑢𝑏𝑢. )−0.124

+0.143 (𝑡𝑧𝑡𝑢. )

θ

𝟐. 𝟐𝟖𝟘 ± 0.123 (𝑡𝑢𝑏𝑢. )−0.124

+0.142 (𝑡𝑧𝑡𝑢. )

θ

𝟐. 𝟐𝟖𝟕 ± 0.123 (𝑡𝑢𝑏𝑢. )−0.124

+0.142 (𝑡𝑧𝑡𝑢. )

No NC disappearance → R = ’s two degenerate best fit points θ23 Δ𝑛32

2

δ𝐷𝑄

–

“ ” Δ𝑛41

2

𝜾𝟑𝟓 𝜾𝟒𝟓

  • ’s

θ23 Δ𝑛32

2

δ𝐷𝑄

  • θ23

δ24

  • 𝜄24

𝜄34

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SLIDE 24

Jeff Hartnell, Solvay 2017 24

𝜉e appearance

(simulated 𝜉e CC event)

  • Identify contained 𝜉e CC candidates in each detector
  • Use Near Det. candidates to predict beam backgrounds

in the Far Detector

  • Interpret any Far Det. excess over predicted backgrounds

as 𝜉e appearance

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SLIDE 25

νe Near Detector Data

Jeff Hartnell, Solvay 2017 25

  • Select νe CC interacAons with 73% efficiency and 76% purity
  • Use ND data to predict background in FD

– NC, CC, beam νe each propagate differently – constrain beam νe using selected νµ CC spectrum – constrain νµ CC using Michel Electron distribuAon

beam νe up by 4% NC up by 17% νµ CC up by 10%

ν

  • ν
  • 𝑡/ 𝑡 + 𝑐
  • –
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SLIDE 26

PredicAon

Jeff Hartnell, Solvay 2017 26

Total BG NC Beam νe νµ CC ντ CC Cosmics 8.2 3.7 3.1 0.7 0.1 0.5 NH, 3π/2, IH, π/2, 28.2 11.2 Signal events (±5% systematic uncertainty): Background by component (±10% systematic uncertainty):

¨ Extrapolate each component in

bins of energy and CVN output

¨ Expected event counts depend

  • n oscillaAon parameters

CP

δ Total events expected

10 20 30 40 50 2 π π 2 π 3 π 2 POT equiv.

20

10 × 6.05 NOvA FD =0.4-0.6

23

θ

2

sin NH IH

NOvA Simulation

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SLIDE 27

νe Far Detector Data

Jeff Hartnell, Solvay 2017 27

  • Observe 33

events

Ø background 8.2 ± 0.8

>8σ electron neutrino appearance signal

CVN=0.991 E=1.63 GeV

Reconstructed neutrino energy (GeV) Events / 0.5 GeV Bin

5 10 15 20 1 2 3 1 2 3 1 2 3

0.75 < CVN < 0.87 0.87 < CVN < 0.95 0.95 < CVN < 1

NH

NOvA Preliminary

FD Data Best Fit Prediction Total Background Cosmic Background POT equiv.

20

10 × 6.05

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SLIDE 28

–

  • ’s ν

νμ

  • 2θ13
  • 𝜾𝟑𝟒

𝟏. 𝟓𝟏𝟓 δ π 𝜾𝟑𝟒 𝟏. 𝟕𝟑𝟒 , δ π

  • δ

𝟒π 𝟑

σ

Joint νe + νμ Fit Contours

Jeff Hartnell, Solvay 2017 28

  • Fit for hierarchy, 𝜺CP, sin2θ23

– Constrain sin2(2θ13)=0.085±0.05 – Constrain Δm2 and sin2θ23 with NOvA disappearance results

  • Global best fit Normal Hierarchy

– best fit IH-NH, Δ𝜓2=0.47 – both octants & hierarchies allowed at 1σ – 3σ exclusion in IH, lower octant around 𝜺CP=π/2

δCP = 1.49π sin2(θ23) = 0.40

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SLIDE 29

–

  • ’s ν

νμ

  • 2θ13
  • 𝜾𝟑𝟒

𝟏. 𝟓𝟏𝟓 δ π 𝜾𝟑𝟒 𝟏. 𝟕𝟑𝟒 , δ π

  • δ

𝟒π 𝟑

σ

Contours

Jeff Hartnell, Solvay 2017 29

Antineutrino data will help resolve degeneracies, particularly for non-maximal mixing. Results planned for summer 2018

inverted% hierarchy normal% hierarchy Θ23%<%45o Θ23%>%45o

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SLIDE 30

Future SensiAvity

Jeff Hartnell, Solvay 2017 30

Lower Octant Upper Octant

Year

2016 2018 2020 2022 2024

) σ Significance (

1 2 3 4 5 =0.022

13

θ

2

, sin

2

eV

  • 3

10 × =2.5

32 2

m ∆ =0.625

23

θ

2

/2, sin π =3

CP

δ Normal

and analysis improvements All projected beam intensiy

NOvA Simulation

µ

ν +

e

ν NOvA joint

  • Max. mixing

Hierarchy Octant CPV Year

2016 2018 2020 2022 2024

) σ Significance (

1 2 3 4 5 =0.022

13

θ

2

, sin

2

eV

  • 3

10 × =2.5

32 2

m ∆ =0.403

23

θ

2

/2, sin π =3

CP

δ Normal

and analysis improvements All projected beam intensiy

NOvA Simulation

µ

ν +

e

ν NOvA joint

  • Max. mixing

Hierarchy Octant CPV

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SLIDE 31

Conclusions

With 6.05x1020 POT, NOvA finds:

  • Muon neutrinos disappear

– Maximal mixing excluded at 2.6σ

  • Electron neutrinos appear

– Data prefers NH at low significance – IH, lower octant, 𝜺CP=π/2 region excluded at 3σ

  • 50% more neutrino data being analysed

– Neutral current events show no evidence of steriles – New νe and νμ results very soon

  • AnAneutrino run underway: results in summer 2018
  • Stay tuned!

Jeff Hartnell, Solvay 2017 31

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SLIDE 32

Backup slides

Jeff Hartnell, Solvay 2017 32

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SLIDE 33

Future SensiAvity

Jeff Hartnell, Solvay 2017 33

Year

2016 2018 2020 2022 2024

) σ Significance (

1 2 3 4 5 =0.022

13

θ

2

, sin

2

eV

  • 3

10 × =2.5

32 2

m ∆ =0.625

23

θ

2

/2, sin π =3

CP

δ Normal

systematic uncertainty improvements 2016 analysis techniques with projected

µ

ν +

e

ν NOvA joint

  • Max. mixing

Hierarchy Octant CPV

NOvA Simulation

Year

2016 2018 2020 2022 2024

) σ Significance (

1 2 3 4 5 =0.022

13

θ

2

, sin

2

eV

  • 3

10 × =2.5

32 2

m ∆ =0.403

23

θ

2

/2, sin π =3

CP

δ Normal

systematic uncertainty improvements 2016 analysis techniques with projected

µ

ν +

e

ν NOvA joint

  • Max. mixing

Hierarchy Octant CPV

NOvA Simulation

Lower Octant Upper Octant

slide-34
SLIDE 34

Jeff Hartnell, Solvay 2017 34

νμ à νe appearance probability

Jeff Hartnell, INSS 2016 34

[PDG, 2014]

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SLIDE 35

MaHer Effect & Mass Hierarchy

  • Coherent forward elasAc scaHering
  • Neutrinos (and anAneutrinos) travel through

maHer not anAmaHer

– electron density causes the asymmetry

  • via specifically CC coherent forward elasAc scaHering

– different Feynman diagrams for νe and νe interacAons with electrons...

Jeff Hartnell, Solvay 2017 35

slide-36
SLIDE 36

Different Feynman Diagrams

  • Amplitude for electron

neutrino interacAon with an electron

  • is not equal to...
  • Amplitude for electron

an5neutrino interacAon with an electron

Jeff Hartnell, Solvay 2017 36

+

slide-37
SLIDE 37

Electron neutrinos and anAneutrinos are affected differently by interacAons with maHer à fake CP violaAon Why does the mass hierarchy affect oscillaAons involving electron (anA)neutrinos?

Jeff Hartnell, Solvay 2017 37

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SLIDE 38

MaHer effect (neutrino case)

  • MaHer effect raises (or lowers) the energy state of

the mass eigenstates

– strength depends on electron neutrino content of each mass eigenstate

Jeff Hartnell, Solvay 2017 38

No matter effect matter effect for NH matter effect for IH Splitting changes size in presence of matter

slide-39
SLIDE 39

AnAneutrino case

  • MaHer effect raises (or lowers) the energy state of

the mass eigenstates

– strength depends on electron neutrino content of each mass eigenstate

Jeff Hartnell, Solvay 2017 39

No matter effect matter effect for NH matter effect for IH Splitting changes size in presence of matter

slide-40
SLIDE 40

Splivngs and mixing angles affected

  • Mixing angles in maHer (θM) are modified by

the mass squared splivng in maHer (Δm2

M)

– e.g. simple 2-flavour case:

– Also see it in full 3-flavour equaAons (a few slides back)

Jeff Hartnell, Solvay 2017 40

slide-41
SLIDE 41

Improved Event SelecAon

Jeff Hartnell, Solvay 2017 41

  • This analysis features a new event selecAon technique

based on ideas from computer vision and deep learning

¨ Calibrated hit maps are

inputs to ConvoluAonal Visual Network (CVN)

¨ Series of image processing

transformaAons applied to extract abstract features

¨ Extracted features used as

inputs to a convenAonal neural network to classify the event

slide-42
SLIDE 42

Improved Event SelecAon

Jeff Hartnell, Solvay 2017 42

  • This analysis features a new event selecAon technique

based on ideas from computer vision and deep learning

¨ Calibrated hit maps are

inputs to ConvoluAonal Visual Network (CVN)

¨ Series of image processing

transformaAons applied to extract abstract features

¨ Extracted features used as

inputs to a convenAonal neural network to classify the event

slide-43
SLIDE 43

Improved Event SelecAon

Jeff Hartnell, Solvay 2017 43

Improvement in sensitivity from CVN equivalent to 30% more exposure

  • This analysis features a new event selecAon technique

based on ideas from computer vision and deep learning

¨ Calibrated hit maps are

inputs to ConvoluAonal Visual Network (CVN)

¨ Series of image processing

transformaAons applied to extract abstract features

¨ Extracted features used as

inputs to a convenAonal neural network to classify the event

[A. Aurisano et al., arXiv:1604.01444]

slide-44
SLIDE 44

Jeff Hartnell, Solvay 2017 44

We consider multiple possible sources of systematic error

In each case:

  • The effect is propagated

through the extrapolation

  • We include those effects

as pull terms in the fit

  • The increase (in

quadrature) of the parameter measurement error is recorded

Systematic* Effect*on* sin2(θ23) Effect on Δm232 Normalisation ± 1.0% ± 0.21% Muon1E1scale ± 2.2% ± 0.81% Calibration ± 2.01% ± 0.21% Relative1E1scale ± 2.01% ± 0.91% Cross1sections1+1FSI ± 0.61% ± 0.51% Osc.1parameters ± 0.71% ± 1.51% Beam1backgrounds ± 0.91% ± 0.51% Scintillation1model ± 0.71% ± 0.11% All*systematics ± 3.4*% ± 2.4*% Stat.*Uncertainty ± 4.1*% ± 3.5*%

slide-45
SLIDE 45

νμ Event SelecAon

Jeff Hartnell, Solvay 2017 45

  • Goal: Isolate a pure sample of

νµCC events less than 5GeV

– Select events with long tracks – Suppress NC and cosmic backgrounds

  • 4-variable kNN used to idenAfy

muons

– track length – dE/dx along track – scaHering along track – track-only plane fracAon

  • ND data matches simulaAon

well for muon variables

Muon ID

0.2 0.4 0.6 0.8 1

Events

3 10 4 10 5 10 6 10 7 10

Simulated selected events Simulated background Data

  • syst. range

σ Full 1- POT

20

10 × ND POT norm., 3.72

NOvA Preliminary

dE/dx Log-likelihood

3 − 2 − 1 − 1

Events

0.0 0.2 0.4 0.6

6

10 × Simulated Selected Events Simulated Background Data

  • syst. range

σ Shape-only 1- POT

20

10 × ND area norm., 3.72

NOvA Preliminary

slide-46
SLIDE 46

Jeff Hartnell, Solvay 2017 46

Fit Checks

Calorimetric energy (GeV)

1 2 3 4 5 Events 5 10 15 20

FD Data Best-fit prediction Background

POT-equiv.

20

10 × 6.05

NOvA Preliminary

Hadronic energy (GeV)

1 2 3 4 5 Events 10 20 30

FD Data Best-fit prediction Background

POT-equiv.

20

10 × 6.05

NOvA Preliminary

Length of main track (m)

5 10 15 20 25 Events 5 10 15 20

FD Data Best-fit prediction Background

POT-equiv.

20

10 × 6.05

NOvA Preliminary

Our best fit oscillation prediction matches other distributions well

slide-47
SLIDE 47

Goodness of fit

Jeff Hartnell, Solvay 2017 47

Events

5 10 15 20 Normal Hierarchy

NOvA Preliminary

POT-equiv. 20 10 × NOvA 6.05

Prediction Data

Reconstructed neutrino energy (GeV)

1 2 3 4 5

contribution

2

χ

2 4 6 8 10 12
  • f fit: 41.6 / 17 d.o.f.
2

χ Total

There is no significant pull in the oscillation fit from bins in the tail

23

θ

2

sin

0.3 0.4 0.5 0.6 0.7

)

2

eV

  • 3

(10

32 2

m ∆

2 2.5 3 3.5

NOvA Preliminary

Normal Hierarchy

POT-equiv.

20

10 × NOvA 6.05 90% C.L. 0-5 GeV Analysis 90% C.L. 0-2.5 GeV Analysis

slide-48
SLIDE 48

Jeff Hartnell, Solvay 2017 48

CP

δ ) σ Significance (

1 2 3 4 5 2 π π 2 π 3 π 2

NH lower octant lower octant n IH NH upper octant upper octant n IH POT equiv.

20

10 × 6.05 NOvA FD

slide-49
SLIDE 49

ScaHering in a Nuclear Environment

Jeff Hartnell, Solvay 2017 49 Reco “q0” (=Ehad,vis) 103 Events

10 20 10 20 10 20 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 10 20 10 20 10 20 1.0 1.0

0.8 < |q|/GeV < 0.9 0.9 < |q|/GeV < 1 0.7 < |q|/GeV < 0.8 0.4 < |q|/GeV < 0.5 0.5 < |q|/GeV < 0.6 0.6 < |q|/GeV < 0.7 0.2 < |q|/GeV < 0.3 0.3 < |q|/GeV < 0.4 0.1 < |q|/GeV < 0.2

NOvA ND Data

NOvA Preliminary

  • Near detector hadronic energy distribuAon

suggests unsimulated process between quasi- elasAc and delta producAon

Similar conclusions from MINERvA data reported in P.A. Rodrigues et al., PRL 116 (2016) 071802

slide-50
SLIDE 50

Jeff Hartnell, Solvay 2017 50 Reco “q0” (=Ehad,vis) 103 Events

10 20 10 20 10 20 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 10 20 10 20 10 20 1.0 1.0

0.8 < |q|/GeV < 0.9 0.9 < |q|/GeV < 1 0.7 < |q|/GeV < 0.8 0.4 < |q|/GeV < 0.5 0.5 < |q|/GeV < 0.6 0.6 < |q|/GeV < 0.7 0.2 < |q|/GeV < 0.3 0.3 < |q|/GeV < 0.4 0.1 < |q|/GeV < 0.2

NOvA ND Data
  • 50% systemaAc uncertainty
  • n MEC component
  • Reduces largest

systemaAcs

– hadronic energy scale – QE cross secAon modeling

  • Reduce single non-resonant

pion producAon by 50% (P.A. Rodrigues et al,

arXiv:1601.01888.)

¨ Enable GENIE empirical Meson Exchange Current Model ¨ Reweight to match NOvA excess as a funcAon of 3-

momentum transfer

MEC model by S. Dytman, inspired by

  • J. W. Lightbody, J. S. O’Connell, Computers in Physics 2 (1988) 57.

ScaHering in a Nuclear Environment