Hadrons as keys to
next-generation neutrino experiments
Kyoto HEP seminar
August 9, 2024
Seungho Han

1νν

Nuclear eﬀects

Primary inte

LeptonZννHadrons＋Z

knock-out

Initial state

V-A curren

Contents

Pauli blocking

Int. w/ a nu

interaction

Figure2.1: Overview
of the impulse ap
measurement@ Super-Kamiokande• Neutron production
the description of neutrino-nucleon inter• Recent charged hadron production measurements
nuclear de-excitation, is not considered2
this process in the description in this stSuper-Kamiokande
Neutron use cases in various rare physics searches3

Gd2(SO4)3

Loaded ~40 tons of Gd-sulfate powderSuper-Kamiokande detector• Water Cherenkov detector (1996~)- Large target volume: 50 kton water- O(1) MeV detection: ~12,000 PMTs
36 m

• Physics analyses:- Atmospheric/solar ν oscillation studies- Rare event searches
Proton decays, Astrophysical ν, DM, …

1 km underground41.4. SK-Gd

Neutron detection @ Super-K• ~99% below-MeV neutrons thermalize and “get captured” by a nucleus

Gd(n,γ) fraction

Capture time scale [µs]2020 2022~

Gd(n,γ) fraction~50% (2020-2022)~75% (2022~)

Gd mass fraction [w%]
Figure 1.14: The expected neutron capture time constant ⌧(n, ) and the Gd(n, )fraction are plotted against the loaded Gd in mass fraction. For the SK-VI phase,5Neutron detection @ Super-K
De-excitaiton:
O(1)
MeV
γ
scattere→ faint e-like Cherenkov ring(s)•1H(n,γ): 2.2 MeV~8 PMT hits
Detection
E ciency

155/157Gd(n,γ): ~8 MeV~30 PMT hits
~20%

~95%

ffi

6

Neutron use case #1: Supernova νSuper-Kamiokande
Neutrino emissions fromVERY DISTANT
Core collapse supernovaeν̄e- p Charged-Current(inverse β-decay)

Radionuclides (mostly 16N)
produced via cosmic µ penetrationµ−p + e → n + νe−n → p + e + ν̄e…

Tag

Signalβ-decays

Background7

Neutron
use
case
#1:
Supernova
ν
4 days with 0.03% Gd➡ Totally 956 days SK-Gd datay Nn = 1
With n-tagging,dominant BG becomes 9Li β+n decays and atmospheric ν
ron ID and background
reduction

Tension from zero assumption

ous
excess, but indicates(min. p-value=0.04)
Spectral-ﬁtting
analysis

M. Harada, Neutrino 2024

-1

Number of events [bin ]20
Spectrum ﬁtting analysis
to extract signiﬁcance
SK-VI+VII observed data (956.2 days)
18 of SK (5823 d pure-water andTotal 6779 days
Atmospheric-ν (non-NCQE)

956 days

16 combined Atmospheric-ν (NCQE)
956 d Gd-water)
9.0 days)
Spallation Li
E)
Analysis threshold:Eν> 17.3 ReactorMeV ν
145823 days

DSNB:
Physics
insight
SK-VI+VII(956 d)

Accidental coincidence
Suppress uncertaintyofbackground
prediction
12
DSNB (Horiuchi+09 6-MeV, Max.)by ﬁtting both10Nn=1, Nn≠1

)

8

6
80

gy
Erec[MeV]
[MeV]

4

2

~1.5σ

Pure water~2.3σ

All data

DSNB: “Relic” neutrinos from past all distant CCSNe
0

1

2

3
4
-2 -1
DSNB flux [cm s ]ΛCDM, cosmic expansion

Highlight:

2~1σ excess

Gd-era∼ 1.5σ excess∼ 2.3σ excess

SKI-VII4

06779 days

What information DSNB ﬂux provide

6
8

SKI-IV204060

dFν̄(E, z) dt
ΦDSNB(E)∝ RSN~1000(z) days exposure is dz
Sensitivityof SK-Gdalready∫80
dE
dz
comparable level it with ~6000 days of pure-water SK

Reconstructed e+ kinetic energy [MeV]

Erec [MeV]SNrate
SN ν isemission
Best ﬁt of whole SK observation
1.4+0.8-0.6 cm-2 s-1

S.Ando et al., Proc. Jpn. Acad., Ser. B, Phys. 99 (2023) 10∋

V, Max.)~1σ∼ 1σ excess

SKVI+VII

for Eν> 17.3 MeV∋

8S.Ando et al., Proc. Jpn. Acad., Ser. B, Phys.

gle 2.2 MeV gamma-ray. The detection efficiency of this gamma-ray at SK

Another advantage of detecting recoil neutrons from neutrino evet approximately 20%. Figure 1.7potential
shows the di↵erencein the expected
neuto enhance
the reconstructionof the incident neutrino kin

nal multiplicity between true neutrino
and antineutrino
events.
It implies
becomesparticularly
valuablefor atmospheric neutrino events, whereNeutron use case #2: Atmospheric ν oscillation(indicativeof classification
path length)and energy are initially unknown. Currentl
uiring no neutron in the event allows
the e↵ectiveof antineu-

as the momentum and direction of scattered charged leptons are util

om neutrinos. It is important to note that the neutron multiplicity in ⌫/¯⌫accuracy in representing incident neutrino kinematics is limited.

events is not strictly confined to zero or one. This variability arises due toKeyvariablesπ,K
Toasachievea moreaccuratereconstruction of neutrino energy, w
nation from non-CCQE events, as well
the influenceof nucleare↵ects(ー) (ー)E:
0.1-100
GeV
the
count
of
neutrons,
expected
tobeproportionate
tothe energy tran
Cosmicp,ννsequent hadronic interactions within the detector medium. These factorseμhadronsystem [26, 27]. Figure 1.10 illustrates
the expected
linear iL:
10-10000km
Atmospherete to the smearing of the neutron signal multiplicity, as evident in the tails

fraction of “missing neutrino energy” with respect to the detected ne

d in Figure 1.7.

ν vs. ν̄ classi cation

Expected neutron signals

Expected missing
hadronic E [%]νln → lpν̄l p → ln

Kinematic reconstruction

Detected neutron signalsfi

Figure 1.10: The expected fraction of energy reconstruction error 9((E⌫

Atmospheric ν are sensitive to Neutrino Mass Ordering

Cosine zenith angle

Normal Mass Ordering

5 5

2-10 GeV νµ passing
Earth mantle and core

Neutrino energy [GeV]10

Atmospheric ν are sensitive to Neutrino Mass Ordering

Cosine zenith angle

Inverted Mass Ordering

5

Appears forantineutrinos

Neutrino energy [GeV]11

pproximately 20%. Figure 1.7 shows the di↵erence in the expected neu-

multiplicity between true neutrino and antineutrino events. It implies

Neutron impact onneutrinos.
It is important
to note that theneutron multiplicity in ⌫/¯⌫ν MassOrdering
sensitivity

ng no neutron in the event allows the e↵ective classification of antineu-

ts is not strictly confined to zero or one. This variability arises due toon from non-CCQE events, as well as the influence of nuclear e↵ects

80%
uent hadronic interactions
within study
the detector medium. These factors
Fake datao the smearing of the neutron signal multiplicity, as evident in the tails
Apply ν and ν̄ separation
Figure 1.7.
0%
via nal-state n, with 80% e ciency

AssumeNormal MO

νln → lpν̄l p → ln
Expected neutron signals

Assume 10 years Super-K operation,
fully-contained atmospheric ν events only

ffi

The probability density of expected neutron signal multiplicity for subfi

AssumeInverted MO

12

Breaking MO/CP degeneracy withaccelerator (T2K) - atmospheric (SK) joint-fit

1.2. Parameter measurements and challenges

T2K νμ → νe appearance data

782 Page 26 of 50

CHAPTER 1. INTRODUCTION

Eur. Phys. J. C (2023) 83:782

T2K-SK joint t sensitivity

e-like in ν̄-mode

Rejection power for no CPVO(MeV) energies, irrelevant to T2K’s analysis. During therun, the water’s attenuation length, as measured by throughgoing cosmic-ray muons, was found to be stable above 90 m,
Potential shift due to consistent with data taken before the refurbishment, albeit
J. Xia, andNOW
2022
slightly
longer.
Thissuggests
event
reconstruction
detecuncertainty in MO
tor uncertainties should similarly be consistent between thedata periods, and several cross-checks were performed
to
T2K+SK
Genuine δCP e ect
confirm this.
Figure 15 shows such a comparison between stopping
cosmic-ray muon data and theirT2K-onlyMichel electrons taken durExpected
Data
ing the run 9 and run 10 data periods at SK. The similarityof the distributions over bothSK-only
data sets highlights the stabilEPJC 83:782 (2023)
ity of the detector and reconstruction algorithm following
the refurbishment in 2018. Though only the reconstructed
Michel momentum distribution and the parent muon’s pare-like in ν-mode
ticle ID parameter are shown in the figure, distributions for
Figure 1.3: Comparison of observed e-like candidates in the T2K far detector (Super13
Figure1.4:
The
expectedpower
toreject
CP
symme
other
reconstructedparameters
used
in
the
T2K
event
selecKamiokande) with ⌫µ and ⌫¯µ beam modes against predictions based on varying
similarhigh consistency.
Kolmogorov–Smirnov
oscillation parameters. The assumed normal mass ordering mimics changes in tion
the showedent
analyses:
SK atmospheric
neutrino data only wit
observed ⌫e : ⌫¯e ratios corresponding to CP = ⇡/2, while the inverted ordering
tests of the expected events in run 10 confirmed this. This wasfiff

Neutron-tagging atmospheric ν may help break the degeneracy even further

mechanisms,
can scatter with
nucleons
or detector
electronsv
through aitneutral-current
channelin thethrougha neutral-current
channel inin Fig.
the detector
via V μboson
exchange, as depicted
3. The scatteringboson
exchange,asdepicted
in
Fig.
3.
The
scatteringrate
22
scales as
ϵ
α
,
where
α
¼
g
=4π.
The
acceleDDD
2 α , where α ¼ g2 =4π. The acceleratorscalesasϵD
produced dark
matter D
event Drate in MiniBooNE scal
produceddark matter event rate in MiniBooNE scales as
44 ϵ αD for on-shell decays in this model.
ϵ αD for on-shell decays in this model.
Another potential dark sector scenario amenab
Another potential dark sector scenario amenable to
theMiniBooNE
searchis
leptophobic
darkmthe MiniBooNE search is leptophobic dark matter[8,10,11,31,32],
in
which
the
mediatorV
couplesd
Supernova νe-e [8,10,11,31,32],
in which the mediator V couples domiBoosted
sub-GeVDM
nantlytoquarksandnotleptons.
For
illustratio
scattering
nantly to quarks and not leptons. For illustration, a

Neutron use case #3: MiscellaneousProton decay,
e.g.

n
16O

(a)

0-n signals

(a)

(b)

(b)

FIG.FIG.
3. Feynman
diagrams
for thefordarkinteractions
with
3. Feynman
diagrams
thematterdark matterinteraction
1-nnucleons
and electronsin MiniBooNE.
The signals
Δ,Thein theboundnucleonsand electronsin MiniBooNE.
Δ, in
the b
nucleoncase,case,wouldwould
be observedby its by
decay
products:a pion a
nucleonbe observed
its decay
products:
and anda nucleon.
a nucleon.

ff

Nuclear e ect modeling important for both bound nucleon signals and ν BG

14

Cone

Bckg

Limit
Bckg
(kT-y) 1
8.4 ± 0.75
0.017
1.6 ± 0.3
1
32.0 ± 1.9240.023
6.3 ± 0.845
72.5 ± 3.5
69
0.078
13.6 ± 1.6
11126.5 ± 5.4 125
0.123
23.3 ± 2.3
18196.8 ± 7.6 202
0.201
35.4 ± 3.3
31283.7 ± 10.1 285
0.214
49.3 ± 4.3
48384.8 ± 12.8 375
0.187
68.1 ± 5.4
67499.6 ± 15.9 Using
494
0.249to separate
90.2IBD± 6.9and 90
Gd-nES
7.59 ± 0.18
5
0.017
1.25 ± 0.07
1
▶

GC 5
GC 10
GC 15
GC 20
GC 25
GC 30
GC 35
GC 40
Sun 5

Limit
(kT-y) 1
0.018
0.026
0.032
0.028
0.049
0.081
0.101
0.124
0.020

Bckg
0.016 ± 0.005
0.060 ± 0.018
0.14 ± 0.04
0.25 ± 0.07
0.37 ± 0.11
0.53 ± 0.16
0.70 ± 0.21
0.90 ± 0.27
0.015 ± 0.004

Neutron use case #3: Miscellaneous
Water cherenkov detector can extract the direction of the SN from the ES interactions

0
0
0
0
0
0
0
0
0

Limit
(kT-y) 1
0.015
0.015
0.014
0.014
0.013
0.012
0.011
0.011
0.015

▷ Separating ES from IBD allows to improve the SN direction pointing accuracy of

Locate supernova

Constrain DM scenarios

the detector
TABLE II. Estimatedbackgrounds,numbers of events in data, and signal event rate limits for each cone and each energy sample.
The event rate limits
areuseatthe
the90% confidence▷ We cancharacteristic
delayed level.
coincidence between the IBD’s positron

Constrain GUT models

e direction on skymap

tested.
reweighting
Using
Gd-nThisto separate
IBDaccounts
and ESfor the model depen-

dent recoil electron energy spectrum, as well as the model
34 years
SK (90% CL): Water
2.4×10cherenkov detector can extract the direction of the SN from the ES interactions
dependent smearing between the boosted dark matter di▶

▷ Separating ES from IBD allows to improve the SN direction pointing accuracy ofCold DM annihilating into lighter DM@ Galactic Center

Dark photon - electron
coupling scale ϵemission and delayed neutron capture to tag IBD events.
- Gd enhance the detectability of the neutron capture.
=

arXiv:1405.7370

rection and the recoil electron direction. The e↵ect ofthe detector
PRLAnnihila'on
120, 221301 (2018)
boosted dark matter scattering o↵ of both electrons and▷ We can use the characteristic delayed coincidence between the IBD’s positron
protons in the Earth is also accounted for, though thisemission and delayed neutron capture to tag IBD events.
e↵ect is negligible forthe events
majority
oftagging
the allowed
NeutronSN burst
w/o IBD
SN paramburst events w/ 72% IBD events tagged/removed= Gd enhance the detectability of the neutron capture. 2
eter space. A binned (10kpc
statistic
was then computed (10kpc simulation)
simulation)
reduction(Expected with 0.1% Gd)
similar to the one described above:

Predicted lifetime [years]
PRD 102, 112011 (2020)

 ✓
◆
O
i
2
The
TransientandVariable
Universe=min 2 Ei Oi + Oi ln+ i2 6/20
,
E
i
i
i
3
X

(2)

with variables defined as before, summed over three bins

SN burst events w/o IBD tagging
SN burst events w/ 72% IBD events tagged/removed
2
CCSN
@
10
kpc
corresponding
to
the
three
energysamples.
The
test(10kpc simulation)(10kpc simulation)
Super-K
simulation\w 0.1w%
Gd(Expected
with subtracting
0.1%
Gd)
statistic was thencalculatedby
the global
2 are presented
Figure 2: Theoretical predictions of proton lifetime for representative GUT models

minimum . Confidence intervals were found by com2
paring the measured
values withthe distributions
guillaume.pronost@cnrs.fr
ofvalues found by many toy Monte Carlo simula-(for the underlying assumptions, see text). (c here represents the coeﬃcient of a Planck suppressed
dimension-5 operator, for details, see Ref. [107].) Current Super-K data rule out the gray shaded
The Transient and Variable Universe
6/20
2
regions. Future projections/sensitives from JUNO, DUNE, THEIA, and Hyper-K
are also specified in

guillaume.pronost@cnrs.fr

1

10

Lighter DM mass 0.2 GeV
Dark boson mass 20 MeV

102

103

Parent cold
DecayDM mass [GeV]
15

OK, how good is our neutron prediction?
~200 arti cial νμ events in H2O(T2K, 2019)

μ
Reconstructed p⃗T [GeV/c]
R. Akutsu, PhD thesis (2019)×10−39

3.5

MnvTunev1
GENIE v3 hA Valencia QE+2p2h

3

GENIE v3 hN Valencia QE+2p2h

2.5

GENIE v3 hA Empirical 2p2h
GENIE v3 hN Empirical 2p2h

2

Data

1.5

Tµ

Multi-neutron xsec

ν-nucleusinteraction
modelsdσ [cm2 × c / GeV / nucleon]
dp

Mean multiplicity~30,000 arti cial νμ events in 21
C8H8(MINERvA, 2023)

1
0.5
0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

μ
Reconstructed p⃗T [GeV/c]
PRD 108, 112010 (2023)
FIG. 8. The same data-extracted cross section as Fig. 7 now compared to various G
Muon Transverse Momentum [GeV/c]

Models largely overpredictneutrons
withlarge
configurations.
Empirical 2p2h
is a tunein thevariability
same spirit as MnvTunev1 performed by
fi

fi

[9].

16

My PhD thesis work
Neutron production measurementwith atmospheric ν interactions in water
Q. WHY models overpredict neutrons?
A. Data with large statistics vs. Di erent set of models!

ff

17

Ingredients• Atmospheric neutrino data18

Atmospheric ν dataset
Gd
H : Gd(n,γ)
ratio
τ(n,γ) [µs]YearLivetime [days]
ν events
Events / day~12 years, ~40,000 events
SK4
SK5
SK6
Pure water
0.011w%
100 : 0~50 : 50~200 µs~120 µs
2008-20182019-2020
2020-2022
3,244.4
461.0
564.4
29,9424,231
5,203
9.23 ± 0.05
9.18 ± 0.14
9.22 ± 0.13SK7
0.033w%~75 : 25~60 µs2022-present-fi• Events in ducial volume, no particle escaping from detector• Non-ν BG: O(0.1)%

19Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty

20

Neutrino event reconstruction
based on lepton Cherenkov ringe-like ring“fuzzier”µ-like ring“sharper”• Energy reconstruction error <2%• Vertex resolution O(10) cm• e/µ mis-PID rate O(1)%21disagreement below 200 MeV is associated with a bug in nuclear binding energ

2“Visible energy” as a proxy for q

calculation, which is fixed in the newer versions of NEUT.

onstruction• Sum of ring energies assuming electron
2

CCQE• Linear relationship with ⟨Q ⟩, ⟨W⟩
CC

Q

2

non

NC

-QE

W

22

0

SK-II

SK-I

SK-III−2

2
Energy scale uncertainty monitored using naturally occurring particles,−4(MC - Data)
/
Data
[%]
(MC - Data) / Data [%]

4

6
4
2
0

−2

−4

−6

<2% uncertainty for E~O(0.01-10) GeV

0
−2

SK-V

SK-IV

−4

2

SK-V

SK-IV

3

3

4

4

10
10
10
Momentum [MeV/c]

Pure water (SK4)0.01w%
Gd
water
(SK6)
Absolute Energy Scale Check in SK-VI

6

2

10
10
10
Momentum [MeV/c]

4

Conventional FV Michel-e2Expanded FV Michel-e
Conventional FV Michel-e

Conventional FV π0 Mass

0

Expanded FV Michel-e0Expanded
FV
π
Mass
0

−2

Conventional FV π Mass

−4

Sub-GeVMulti-GeV
Stopping Muon
Stopping Muon

Sub-GeV
Stopping Muon
Expanded
FV π0 Mass

2

−6
10

3

10
2

10

3 10

10

3

2

4
4

10

102
10

10
3

10

Momentum[MeV/c]Momentum [MeV/c]

Momentum [MeV/c]

4

10
4

10

Multi-GeV Stopping Muon
923

Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty• O(1) MeV neutron detection algorithm + e rejection

24

Neutron signals in atmospheric ν event

SK-VI MC: ¯ CCQE, E = 0.63 GeV

ν̄μ+ 16O →

μ → eeMichel+μ + n + 15N
Gd(n, )

Up to~535 µs

Mostly PMT noise

25

Figure 4.3: A PMT hit time distribution for a typical ⌫¯µ charged-current quasi-

Neutron signal detection algorithm(Step 1) PMT hit trigger
NHits(Step 2) Neural network

Candidates

Noise vs. Signal classi cation
using NHits, positional correlation, etc.
t
Signal cut

BG radioactivity

Neutron signal

fi

26

Michel electron rejectionneNeural network output

PMT hits

Signal cut
Neutron cut
e nTime from event trigger [µs]27Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty• O(1) MeV neutron detection algorithm + e rejection• O(1)% level validation of predicted signal ef ciencyfi28

Efficiency estimation using neutron point source
Am/Be source: n + γ (4 MeV, BR~55%)
4 MeV γscintillationγnDetected n signals
E ciency ≈4 MeV scintillation triggers
1n control

Bias due to n-induced scintillation, e.g.
- n inelastic interaction- n-capture γ

ffi

29

Simplified simulation has failed to describe data
Neutron particle-gun MC
Pure water (2016)

0.01w% Gd-water (2020)

Neutron e ciency

Predicted(Neutron particle-gun MC)

15-20%~10%
Observed(Am/Be data)

Neutron source position

Date

ffi

30

Efforts to simulate calibration setup

Geant3 → Geant4• Measured n intensity and KE:

110 n/sec (1γ1n) + 128 n/sec (0γ1n)Total uencefl

NIMA 1057, 168701 (2023)

KE, ratio• Fine-tuned optical parameters,

using actual Super-K Am/Be data
Light yield

Pulse shape31

Efforts to include potential biases in simulation
Implement continuous source activity based on measurement (~238 n/sec)
Full Am/Be MC reproduces observed pile-up BG quite well

Events / sec

Signal region
1n control ~95%
Mimicking
BG events ~5%

Event triggering charge [p.e.]

Sideband
2 MeV
H(n,γ)

4 MeV
8 MeV
Am/Be γ Gd(n,γ)
n-inelastic

Max. PMT hits within 3 µs

32

Neutron efficiency: Full Am/Be MC vs. Data
Gd 0.01w%

Pure water

E ciency ~20%

E ciency ~50%

Data
MC

NeutronE ciency(stat+syst)

MC
Data

~10%
WHY?
OK

Am/Be neutron source position [m]

ffi

ffi

ffi

33

MC (H:Gd=50:50)
Data

H

Gd

Signal PMT hits (~γ energy)

Estimate
H:Gd ratio

Gd (n,γ) ratio

Looks like Geant4 has a problem with H:Gd(n,γ) ratio!
Am/Be data
44±3%

Geant4
52%~44%
ENDF/B-VII.1

Gd concentration [ppm]Thermal (n,γ) cross sections (ENDF/B-VII.1) explain Am/Be data very wellGeant4.10.5 MC predicts ~15% more Gd(n,γ), boosting predicted e ciency by ~10%
Corrected via calib/MC scalingffi

34

Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty• O(1) MeV neutron detection algorithm + e rejection• O(1)% level validation of predicted signal e ciency• Data: Neutron multiplicity estimation

ffi

35

True neutron multiplicity estimation
True neutron multiplicity ≈of a neutrino event

Detected neutrons − Estimated BG
Estimated neutron e ciency

Neutron e ciency

Regression based on neutrino MC
Am/Be calibration data vs. MC

MCRegression
model t (1σ)

Correction scale

SK-IV

1.1000 ± 0.1055

SK-V

0.9734 ± 0.0710

SK-VI (Gd)0.9086 ± 0.0352

ffi

Neutrino Evis [MeV]SK phaseffi

fi

Evis, ν vertex, lepton PID, #Ring36

Mean neutron multiplicity

Ef ciency-corrected

Neutron multiplicity:
Phase consistency++ Pure water: 2008-2019++ 0.01w% Gd-water: 2020-2021
Phases with/without Gd,
having di erent neutron e ciencies,
show consistent results
Variations added assystematic uncertainty

ffi

ff

fi

Visible energy [MeV]37

With the exception of extremely low-energy events, the most prominent source of
uncertainty is the uncertainty in the signal efficiency mean, approximately ⇠ 15%.
Figure 4.35 displays the mean neutron capture multiplicity distributions of com-

Combined result

bined data with systematic errors. A clear linear relationship between the mean

(stat+syst)

neutron multiplicity and the event visible energy is evident.

y=ax+bCHAPTER 4. NEUTRON MULTIPLICITY MEASUREMENT

x-linear

x-log

Uncertainty

Figure 4.35: Mean neutron capture multiplicity distributions from combined data,plotted against event visible energy. The distributions are shown with both linear(left) and logarithmic (right) horizontal axis scales.

Total

~16%

multiplicity distributions from combined data,— Signal e ciency scale~15%
y. The distributions are shown with both linear— Signal e ciency regression model ~4%
ntal axis scales.
— Neutrino event reconstruction— Statistical

~3%
~4%

}

~16%
223(syst)

ffi

ffi

38

Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty• O(1) MeV neutron detection algorithm + e rejection• O(1)% level validation of predicted signal e ciency• Data: Neutron multiplicity estimation• Various model predictions

ffi

39

Hadron modeling in typical ν event simulation
Neutrino event generator (target nucleus)
l−
νl

W
n

Geant3/4

n

+

p

p

p
π

Final-state int. (FSI)+Nuclear de-excitation• Nuclear ground state• Nucleon correlations (2p2h)• Quark hadronization (DIS)…

Important for low-energy hadron production,
uncertainty poorly quanti ed in the past neutron studiesfiPrimary int. (PI)
(W/Z exchange @ N/q-level)

Secondary int. (SI)(within detector vol.)40

GeV, which aligns with the size of nucleons, this model is

Intranuclear
cascade(INC)
model
tively, similar to the factorization of nucleon-level interac-

Widely usedinteraction.
to describe hadron-nucleus inelastic scattering in 0.1-10 GeV range
ino-nucleus
Semi-classical model: “cascade” of binary collisions with “free” nucleons
p

p

n
n

p

+

0

n

0

n

n

n
p

p

+

• Several models exist and di er by:- Individual nucleon potential/tracking- Free nucleon cross sections- Pauli blocking- Coupling with de-excitation model
Varying some model parameters by 1σ
is NOT enough for uncertainty evaluation

ff

Figure 2.16: Possible scenario of final state interactions.

41Model combinations to compare with data
PI

FSI

SI

• NEUT 5.4.0• SK-VI default• NEUT 5.6.3• SK-IV/V default• GENIE- hA- hN- Bertini- INCL

Flux: Honda ux (2011)
GENIE 3.4.0 G18_10b_02_11b

fl

6 models“Default” models• GCALOR
6×5=30 combos• Bertini• INCL

More sophisticated INC model
Tracks all nucleons simultaneouslywith varying Coulomb/nuclear potential
PRC 87, 014606 (2013)

5 models42

How to predict mean neutron multiplicity(without full MC)⟨n-captures / hadron⟩(x
Outgoing hadron momentum(after FSI) [MeV/c])=⟨ ν event ⟩#nIncoming hadron momentum[MeV/c]43
ff

Hadron∈ {n, p, π±}Stark FSI model di erences below 1 GeV/c

Hadrons / eventΣ

Detector simulator(water volume)

Neutrino event generator(neutrino target 1H/16O)

Ingredients• “Fully-contained” atmospheric neutrino events• Neutrino event reconstruction and uncertainty• O(1) MeV neutron detection algorithm + e rejection• O(1)% level validation of predicted signal e ciency• Data: Neutron multiplicity estimation• Various model predictions

ffi

Let’s go through the results

44

provides a visual comparison of the data with the linear model, assuming fitted
and intercept from the best model combination.

Data
MC

Mean
Neutron multiplicity

Data vs. Models
PI/FSI

Data (stat+syst)
Default simulation

ν
ν/ ν̄

Model variability
ν-nucleus PI/FSI+ Hadron SI

Leptons

Nuclear eνZ

HadronsSI

knock-out

Initial stat~30% less neutrons observed,
interaction
Pauli
bloc
beyond ν-nucleus PI/FSI model variability
Hadron SI models show large variability

Neutrino event visible energy [MeV]

Figure 2.1: Overview of45
the description of neutrin

Visible energy4.4.vs.
PI･FSI/ multiplicity
SI contribution
Averageneutron capture
Default SK-IV MC simulation

ν

SI
PI

FSI

y=ax+ba (slope):
Determined by SIb (intercept):
PI/FSI dominant

SI origin
PI/FSI
originOur data (slope)is Meansensitive“SI”multiplicity
models plotted against visible energy, eFigure 4.42:
neutronto
capturetracted from default simulation models (NEUT 5.4.0 for PI/FSI and SK detecto
46
simulator skdetsim in Geant3.21 with SK-IV default model configuration for SI

Fitted Slope: Data vs. Models
Intercept [neutrons]

G4Bertini and its variants]

ν-nucleus

INCL

models(PI/FSI)]

Hadron SI
modelsSlope [neutrons / GeV]

Phys. Rev. 131, 1801 (1963)
NIMA 804: 175-188 (2015)
PRC 87, 014606 (2013)47

Worst INC model (G4Bertini)(Default model in Geant4,
used in MINERvA 2019/2023)

Best INC model (INCL)
Past neutron de cits
(T2K 2019, MINERvA 2019/2023)
may have been due topoor choice of INC model for hadronic SI~60%

INC model uncertainty ismuch larger than we thoughtfi

48

What causes variations in model predictions?
G4Bertini

INCL

1. Nuclear de-excitation (0-100 MeV/c)
Nucleon emission probability,
variations in residual energy, cluster formation
2. Pauli blocking (100–250 MeV/c)
No blocking vs. strict vs. probabilistic
3. Nuclear effects (250-1000 MeV/c)
Nucleon correlations(repulsion, removal effects, etc.)

Predicted neutron momentum after ν int.
(after FSI) [MeV/c]
ff

Neutrons / Atm. ν event

Models largely di er in low-E (below 1 GeV/c) neutron production49

m. The histograms represent the Bertini model predictions and the data (dots) are from

4. Interfaces

Indeed, Geant4 Bertini cascade model states that…

4.1. Precompound model

As mentioned in Section 2.2, the pre-equilibrium and nuclear
evaporation models native to the Bertini code are relatively simple andhighly parameterized. A more detailed option, G4PreCompoundModel[32] offers better precision at energies below 75 MeV, although at thecost of greater CPU time. In order to use the extended Bertini cascade
for intermediate energies and a more precise model atNIMA
low
energies,
804: 175-188(2015)
an interface connecting Bertini (the main G4CascadeInterface
class) and G4PreCompundModel was developed. It can be activated in50
user code by invoking the usePreCompoundDeexcitation()

16.4
E
/keV
-> ﬁnd out the+0.6
best cascade-deexcitation pair.
+3.4
0
*
±
±
27.9
1.5
15.6
1.3
)beam
INCL++/ABLAv3p0*−2.6−1.0is currently “Bertini cascade”[6].
• N.B.: SKG4 default
Count /8.0 keV/ µC/sr

INCL++/FB

31.1

G4PreCompundModel

γ

22.9
8.7
Data
+0.6
INCL
±
27.9 ± 1.5+3.4
15.6
1.3
−2.6
−1.0

16
del
22.9
8.7
SK
efforts
tocharacterizeOde-excitation
excitedstatesaremissingin
ABLA
5. Deexcitation Models
+3.4
+0.6
Comparedde-excitation
models
w/
the
E525
data
(cascademodel:
INCL++).
•±±
Hadronemission
γ evaporation
27.9
1.5
15.6
1.3
)Down
ep
decays. Open:Multistep
decays
↓
−2.6
−1.0
to
0
MeV
Observed Energy in Ge detector
True Generated Gamma E @30 MeV
M frame RCNP. Particles from N*
Preliminary
excitedstates aretistep Preliminary
decays↓ missing in ABLA

excitation
10
Experiment(RCNP)
4

BIC* Low-lying discrete excited states are missing in ABLA
BERT

10

3

20 < Ex < 40 MeV

1/2 for
BLS’s α41035

303000
3

× 1/2

4000

NucDeEx v2.1
INCL++/FB
INCL++/ABLAv3p
G4PreCompoundModel
5000 Yosoi6000
7000
8000
et al.INCL(CASCADE)- Energy
G4PreCo/keV
p beam exp. (Yosoi et al.)25

INCL - ABLA

stat.
err. only
INCL- NucDeEx v1.3

tistep decays↓
NucDeEx v2.1
2
1020

3.5

× 10

E525 Data
INCL - G4PreCo

20 < Ex < 40 MeV

15Particles from N*
10

Y. Hino, Poster @ Neutrino 2024

−6Count /40.0 keV/neutron

Proton s1/2-hole event/0.01
state
MeV
Branching ratio (%)

1/2

S. Abe, PRD 109,15036009 (2024)

20 < E < 40 MeV

15

10

10

3.0

2.5

2.0

INCL - ABLA

INCL - NucDeEx v1.3

1.5

20 < Ex < 40 MeV
1.0

INCL++/FB
5
1
INCL++/ABLAv3p
15
2
4
6
8
10
12
14
16
18
20
00
n G4PreCompoundModelpdt GammaαE /MeVαObserved γ energy [keV]Energy /keV
ABLA
shows
a largeoverestimation
for
water
detector!
NucDeExNew
simulation
code(NucDeEx)in E > 6 MeV.inInappropriate• stat.
Yosoi
et al.v2.1(CASCADE)
err. only
30 MeV neutron-water scattering
5N* • G4PreCompound
andNucDeExshows
betteragreement.
->
Check
other
energies.
INCL++/FB
16
15
pO(p,2p)
beam N*
exp.
(Yosoi et al.)measurement
51
The bestorcomparable
to
the
best
0 / 10
INCL++/ABLAv3p

Particles from

N*

0.5

0.0
2000

3000

4000

5000

6000

7000

8000

9000

10000

More hadrons for ν CP/MO
Recent hadron production measurements
for next-generation neutrino oscillation experiments

52

Upcoming “long baseline” experimentsfor ν CP/MO test
ESSνSB 🇪🇺
DUNE 🇺🇸
Beam power ~2.4 MW
E ~ 2-4 GeV
L ~ 1300 km
Target ~40 kton liquid Ar
Budget ~500B JPY?
Starting 2031?

Beam power ~5 MW
E ~ 0.3 GeV
L ~ 360 km
Target ~500 kton water
Budget ~300B JPY?
Starting 2037?

T2K/Hyper-K 🇯🇵
Beam power ~1.3 MW
E ~ 0.6 GeV
L ~ 300 km
Target ~200 kton water
Budget ~100B JPY?
Starting 2027?

Expect ×O(10) improvement in statistics

53

Accurately modeling ν interaction is the key
to the next-generation ν CP/MO tests

νμν̄μWith Hyper-K:~2000?

~2%

S. Dolan, Status of T2K Oscillation Physics, νPhys 2023

EPJC 83:782 (2023)
54νl2.1. PRIMARY INTERACTION

l−

ν

ν
21

νl

l−

Hadron modeling impact#1:
Neutrino
energyreconstruction
Z
W
W
π
0

+

+

+

inelastic scatterings with a quark, called deep inelastic scattering (DIS), take place. Fur5.2.3 SingleParameter
Central Value Uncertainty++∆
thermore, in the energy region between
QE
and
RES,
known
as
the
dip
region,
the
2p2h
1.03
0.06
p/n
p/n
p
p M (GeV)p
The uncertan
D E (MeV)+4
6
cal to previo
interaction, which is the scattering with two correlated nucleons, is considered to exist. Multi-nucleon
D E (MeV)
06knockout tral paramete

2-body quasi-elastic

l−

ν

ν

νl

l−

Z0

W+

21
p
n
c scattering (DIS),(a)takeplace. FurCCQE
known as the dip region, the 2p2h
Δ resonanced nucleons, is considered to exist.
16O)−(πabsorbed
in
νl
l
ν
νl
l−
W+
π+
A
W+
π+
A
++
∆
p/n
p
p
(d) CCCOHp/n
A

T2K simulation
CC0π
W+
π+

p/n

(b) NCQE(d) CCCOH

A

Eν ≈ E(Elep, θlep)(b) NCQE

νl

νl

p
N

+2
(c) CCRES
0

l−

W∆+++
(c) CCRES(e) CCDIS

1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

0.05 < Q2 < 0.10

νl

l− +
π

W+

6
6

(nucleons invisible)
0.10 < Q2 < 0.15
0.15 < Q2 < 0.20
0.20 < Q2 < 0.25
0.25 < Q2 < 0.50
0.50 < Q2 < 1.00
Q2 > 1.00

W+

—
—
—
—
—
0.11
0.18
0.40

l−
p

Tab. 3: The parameters included in the 1p1h uncertainty
model with their values and uncertainties before the ND
analysis. The uncertainties for the removal energy parameters are around their central value and contain the carbonoxygen and n-n correlations described in the text. The first
five Q2 parameters are not externally constrained before theanalysis, and are free to vary between 0 and •. The units of
the Q2 ranges are GeV2 .

nphadrons p/np/n(f) CCLarge
di 2p2herenceνin modelMartini,
predictions−
νldiagrams of major neutrinolFigure 2.2: Feynman
interactions.
−2p2h predictions EPJ C 83:782 (2023)lµνl

σ(Eν) (cm2/ 12C nucleus) ×10-38

νl(a) CCQE
Assume2-body QE,

QE
A
nO
rmv
nO
rmv
nC (MeV)D ErmvnC (MeV)D Ermv
0.00 < Q2 < 0.05

3

Martini, νµ

2.5

Nieves, ν+WThe eﬀective Lagrangiandensity of neutrino-nucleon interactioniswritten
by
+
SuSA
v2, ν
p
W
n
ν
N
hadrons p/n !
"
p/n
GF †
′ λ
′
Bias in reco.Lefenergy
√
=
j
(k,
k
)J
(p,
p
) + h.c. ,fλ2(e) CCDIS(f) CC 2p2h
µ

2

µ

1.5

µ

How much is the non-QE fraction?

5.2.4 Deep i

1

0.5
0

0.2

0.4

0.6

0.8

mass, MARES ;ferred four-mof the I1/2 n
rameters hav
using NUISA101] and BN
The uncertai
describes theregions fromsurements oand MINER
A new pactions prod
backgroundfreedom is ation paramet
actions with
The paramet
certainty of 1
Normalis
cross section
an uncorrela
MINERvA d
ing is fully c

(2.1)
1

Eν (GeV)DIS interacti
this oscillati
ertheless, un
matics fromfit, whose se
been signific
As discu
rections to cin the BY c
difference be

ff(c) Figure
CCRES2.2: Feynman′of major
wherediagrams
GF is theFermi neutrino
constant,interactions.
jλ is leptonic current, Jλ Fig.
is 9:hadronic
current,k(k
Cross-section predictions
for nµ (solid)and)n µ12 C from Martini et al. [96],(dashed) 2p2h interactions
on′ ) represents the55
represents the four-momentum of the initial (final) state lepton,
p and(pSuSA
Nievesandet al. [60],v2 [97, 98].
S. Dolan, International Neutrino Summer School (2023)

Hadron modeling impact #1: Neutrino energy reconstructiona.u.

Eν ≈ Elep +

∑

Ehad

DUNE
simulation

How much energy doneutrons take away?
~ Neutron multiplicity?
Events withintranuclear neutron production

Bias in reco. energy

ffi

S. Dolan, International Neutrino Summer School (2023)

Neutron detection e ciency@ liquid Ar TPCs: <10%(arXiv:2406.10583)

56

To better quantify these deviations, the double ratio ofthe differential cross section predicted by two different
models is computed as

HF-CRPA=SF
HF-CRPA=SF, RRν̄e =ν̄μ,RRνe =νμHadron modeling impact #2: Neutrino cross sectionin Fig. 2. The forward-scattershow a large discrepancy bet
Model 1is interesting to see that
R
ðE
;
θÞ
ν
να =νβModel 1=Model 2
negligible
whenconsidering
RR
ðE
;
θÞ
¼
:
ν
να =νβ predictions,Model
2 we rely heavily on theory
appearancebutReνα =νβ ðEν ; θÞνe xsec crucial for ν
FSI as mean eld/potential
a ects cross section

σ(νe)/σ(νμ) pred. di . [%]σ(νe)/σ(ν̄e) pred. di . [%]4

How should we model FSI?
FSI independent fromneutrino-nucleon interaction

⊗

ff

ff

ff

fi

fi

ff

Model-to-modeldierencesG. 3: The flux-averaged uncertainties inpercent
obtainedbycomparingthedi↵erent
sectionthe
modelsFIG. 3. The flux-averaged uncertainties in percent obtainedcrossby comparing
differentshown
cross-section
57(forHyper-K
conguration)
PRD
108, L031301(left),
Δ(center),Δ(right).
The
lower
triangle
is averagedover
over thethe event-rate
distribution prein table
I: ⌫e /⌫(2023)(left),(centre),(right).
The
lower
triangleisaveraged
event rateν̄e⌫=ν̄νe =ν̄e
⌫
¯e /¯
⌫µ
μ ⌫e
µ
e /¯

Hadron modeling impacts
on δsensitivity
CP violation
CP sensitivity• Fraction of δCP values excluding CP conservation as a function of exposure• Exclude CP conservation to 5σ for >60% of true δCP values, >75% to 3σ
4160

δCPd@°D[deg]

140
120
100

Calorimetric Method
Realistic Resolution

Biased value

Correct Result

90%
80%
70%

80

H c2bf êdof=0.3ê106L

Missing
EnergyH c2bf êdof=1.4ê106L
H c2bf êdof=3.7ê106L

True valueË

60
Contours for Dc2=2.3

40
6.5

Wide Band Beam, L=1300 km

7.0

7.5

q13@°D
8.0

Charlie Naseby

8.5

9.0

9.5

FIG. 2: E↵ect of an underestimation of the missing energy
in the calorimetric energy reconstruction on the confidence
regions in the (✓13 , ) plane, see text for details. The true

δCP coverage [%]PRD 92, 091301 (2015)

Assumed σ(ν )/σ(ν̄e) error

is by using a Gaussian function with a p
simple function for
its standard deviation: (E⌫ ) = ↵ +E⌫ + E⌫ , where eE⌫ is the true neutrino energy in GeV. Typical values
used in phenomenological studies
p of liquid argon detector
experiments are (E⌫ ) = 0.15 E⌫ , see e.g. Refs. [43–46]. In our case, we use the migration matrices whichhave been obtained from the event generator, and fit theresult to a Gaussian with a width in the above form. In
the case of ⌫e DIS events (i.e., the dot-dashed curve in
Fig. 1), the best-fit to the matrices is given by a Gaussianpwith standard deviation (E⌫ ) = 0.158E⌫ + 0.13 E⌫ .
Finally, the dashed curve, obtained from linear interpolation between the dot-dashed and solid lines, represents
an intermediate situation in which 50% of the missing energy is accounted for: the two distributions used in thelinear interpolation do have the same width, while theircentral value di↵ers due to the impact of missing energy
Imperial College London
25 Aug 2023
in the events. It should also be noted that, for each typeof neutrino interaction considered in this work, the widthof the distribution obtained when computing the migration matrices is generally di↵erent.
Based on Monte Carlo studies, the hadronic energy uncertainty in the MINOS experiment has been estimatednot to exceed 8.2% [47]. However, in view of the re-

5σ CPV
5%discovery

0% 3%~5 years

for50% δCPcoverage

Xsec systematics: key to early CPV discovery
58

T

0.14
0.12
0.1

10

0.5

0.2

0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Reco Q2 (GeV2/c4)

0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Reco Q2 (GeV2/c4)

How good are we in modeling hadrons?
2p2h
RES

45 < ↵T < 90

0.08

0
0

]

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

μ
Total hadronic energy

T

2
1.8

0.25

Total: 2 = 23.62

0.2

2p2h

135 < ↵T < 180

0.1

Neutrino Beam
Neutronsνµ CC
Selectionνµ +Directq < 0.4 GeV/c
3

ND Data
Empirical MEC
Other
Valencia Neutrons
MEC
Reinteraction
QE
Data
RES
DIS
Other
Muon

1.2

1 10

0.8
0.6
0.4

8
6

0.05

0
0

Data / MC

0.2

00 4

1.5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

p [GeV/c]
T

1

2

0.5 0
0 0.0

7

EM and Hadrons

4

T

RES

14

8

MnvTunev1

1.4 1210 Events

CCQE

0.15

1.6

candidates / event

MicroBooNE

Neutron energy

10

20

GEANT Modification

30

40

50
60
Reco Energy Deposit [MeV]GENIE Modification

10 0.1200.2

candidates / event

Lepton-nucleon
p [GeV/c]kinematic correlations

0.02

applied, including the addition of the fitted 2p2h described in Sect. 4.3,
except that the RPA-like low-Q 2 suppression is not applied to RES
interactions in the top plots. Neutrino beam is shown at left, antineutrino beam at right
PHYS. REV. D 108, 112010 (2023)

6
5

4

3
2

Data / MC

0.04

Fig. 2 Reconstructed Q 2 distributions in the reconstructed W rangeof 1.2 to 1.5 GeV/c2 , where RES events dominate. Data are shown withstatistical error bars, while simulation is shown as histograms stacked byinteraction type. All crossOF
sectionadjustments
described in this paper
0.9 MEASUREMENTTHE
MULTINEUTRONν̄ are…

104 Events

0.06

-38
2
d2
10
cm
[
]
d Td p GeV/c deg Ar

Other

0.6
0.4

1.0

CCQE

0.18

Other

1.5

Total: 2 = 19.63

0.2

0.9

9

MicroBooNE

0.22

0.16

10

-38
2
d2
10
cm
[
d Td p GeV/c deg Ar

0.24

1

300.3

400.4

energy deposits [MeV]

50
0.5

60
0.6

Visible Ehad (GeV)2
1.8

MnvTunev1

0.4 GeV/c < q < 0.8 GeV/c Antineutrino Beam

Neutrons
Selectionνµ + νµ CCDirect3

1.6

EM and Hadrons

ND Data
Muon
Empirical
Other MEC
Valencia
MEC Neutrons
Reinteraction
QE Data
RES
DIS
Other

1.4
1.2
1
0.8
0.6
0.4
0.2
00

1.5

1

0 0.5
0.0 0

10

20

GEANT Modification

30

40

50
60
Reco Energy Deposit [MeV]GENIE Modification

100.1

20 0.230

40
0.3
energy deposits [MeV]

50 0.460

Visible Ehad (GeV)

FIG.
Neutron ofcandidates
event inin binsof energy
deposited
at(left)q3 <0.4
GeV=c (left)
and 108,
0.4< q3 <
GeV=c (right)
MINERvA,
PRD
112010(2023)
Fig. 34. Comparison
ND data toper
simulation
reconstructedvisiblebeam
andantineutrino
beam(right).
TheGeV=cfilled,stackedhistogramsof pT andarXiv:2407.10962↵T from MicroBooNE,
compared with
(MicroBooNEνµ predictions
CC1p)
NOvA,
EPJC
80:1119(2020)
where
q
is
the
magnitudeofthe
3-momentum
transfer.
In(solid
the top panels,
rate predictions
broken to
down
into
hadronic3energy using the default GENIE empirical MEC model
indicateMC
the candidate
non-MEC componentsof theareprediction,whichallthethe type ofthe breakdown by interaction mode into QE, 2p2h and resonantparticlethat
wasValència
reconstructed
as a(dottedneutroncandidate.
The bottommodifications
panels compare
the indata/MC
ratiobeen
to twoalternative models. Error
red curve)or the
MEC model
black
curve), in neutrino
described
Sect. 4 haveappliedbands on the data points include only statistical uncertainties. The pink uncertainty envelope on the MC lines carries all systematic
uncertainties discussed in this paper.

hat
eaenng
.

Large
room
for
improvement
existsinmodelingoflow-E
hadrons
where FSI is expected to be less impactful. In fact, before
the phase spaceconstraints definedin multi-nucleon
Table I, theThe
totallower panel correlationsof Fig. 4 highlights theanddifference
momentum transfer
similar to Q in this kinematic region
producedvia
intranuclear
interactions.
cross section predicted by the SF(SF*) model is about2

between ME-era neutron tagging data and simulation.
5%(10%) higher than that predicted by the LFG
model
The
Monte Carlo over-predicts the rate of neutron candifor T2K(MicroBooNE). After applying the low proton

without introducing additional dependence on neutron
59
modeling [48]. It is also easier for models123
to predict than

its envalue
consi
effectbetter
predi
lowes
and
<35 MeV ~250 MeV/cvariabthis hneutr
Dataciallythe inIn
NEUT predicts much less low-E nucleons;surem
Strict Pauli blocking + cuto energy for FSIof lepthe crfor aloffersNEUT
dueinterato stim
FIG. 26: Result forProton
the extractionof the ⌫µ CC cross section
to mamultiplicity
Proton KE [GeV]
as a function ofMicroBooNE,
proton multiplicity.
The
inner
error
bars
on
lation60
PRD 110, 013006 (2024)
MicroBooNE,
PRL cross134, 041801(2024)
FIG.
2.
The
unfoldeddifferential
sectionfortheinclusivethe data points represent the data statistical uncertainty, andsearc
for the 0p bin where only GiBUU is able to describe the2data. This gives GiBUU the lowest χ despite its underprediction of the data at moderate-to-high energies. Similarµunderprediction is seen for GENIE and NuWro in thisregion. NEUT and the μBooNE tune describe moderate-toGenerator that uses BUU transport eq. high energies well, but NEUT also significantly under2
the 0p bin,contribution
increasing itsfrom
χ . 0p events(instead of INC) matches low-E data 38 predicts Includes

ff

dσ/dKpProtons in ν CC events @ Liquid Ar TPC

its envalue
consi
effectbetter
predi
lowes
and
<35 MeV ~250 MeV/cvariabthis hneutr
Dataciallythe inIn
NEUT predicts much less low-E nucleons;surem
Strict Pauli blocking + cuto energy for FSIof lepthe crfor aloffersNEUT
dueinterato stim
FIG. 26: Result forProton
the extractionof the ⌫µ CC cross section
to mamultiplicity
Proton KE [GeV]
as a function ofMicroBooNE,
proton multiplicity.
The
inner
error
bars
on
lation61
PRD 110, 013006 (2024)
MicroBooNE,
PRL cross134, 041801(2024)
FIG.
2.
The
unfoldeddifferential
sectionfortheinclusivethe data points represent the data statistical uncertainty, andsearc
for the 0p bin where only GiBUU is able to describe the2data. This gives GiBUU the lowest χ despite its underprediction of the data at moderate-to-high energies. Similarµunderprediction is seen for GENIE and NuWro in thisregion. NEUT and the μBooNE tune describe moderate-toGenerator that uses BUU transport eq. high energies well, but NEUT also significantly under2
the 0p bin,contribution
increasing itsfrom
χ . 0p events(instead of INC) matches low-E data 38 predicts Includes

dσ/dKpProtons in ν CC events @ Liquid Ar TPC

ff

No model fully describes data!

Key messages• Understanding nuclear e ects in ν-nucleus interaction is criticalfor both ν oscillation physics and rare event searches involving nucleus↪︎ We need accurate modeling/measurement of outgoing hadrons• Neutrons are especially important in Super-K; (also for Hyper-K, DUNE)
I highlighted: - Signi cant uncertainty in low-E hadron transport- Widely used models inconsistent with SK atmospheric ν data
40
High-quality
low-E
protonmeasurements
arecominginforAr target,•
further showing inaccuracies in currently available models

ff

fi

•

Stay tuned for NINJA p, π measurements with νµ-H2O!

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