GENIE Models and Global Fits of Neutrino Scattering Data

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE models and global ﬁts of neutrino scattering data

Marco Roda - [email protected] on behalf of GENIE collaboration

University of Liverpool

27 September 2017 NUFACT 2017 Uppsala

1/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Generators Neutrino MC Generators: A Theory/Experiment Interface

Access the ﬂux distorsion due to oscillation Every observable is a convolution of ﬂux, interaction physics and detector effects

Connect truth and observables Event topologies and kinematics Model dependencies

Good Generators ⇒ Support oscillation analyses uncertainty validation tune the physics models

⇒ Tuning proved to be difﬁcult So far no results

Several MC Generators in use: GENIE, GiBUU , NuWro, NEUT

2/49 Global ﬁts Generator is the ideal place for global ﬁts We control the model implementation

Constraints on Cross Section for oscillation analysis Neutrino Cross sections priors Eventually based on data

What generators can do depends on the available datasets

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Role of generators Roles of MC generators in Oscillation Physics

Comparing data and models ⇒ You cannot study oscillations without fully understood models Validity region Highlight tensions

Feedback for experiments Drive the format of cross section releases Hint toward key measurements

3/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Role of generators Roles of MC generators in Oscillation Physics

Comparing data and models ⇒ You cannot study oscillations without fully understood models Validity region Highlight tensions

Feedback for experiments Drive the format of cross section releases Hint toward key measurements

Global ﬁts Generator is the ideal place for global ﬁts We control the model implementation

Constraints on Cross Section for oscillation analysis Neutrino Cross sections priors Eventually based on data

What generators can do depends on the available datasets

3/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Dataset Evolving datasets - Old datasets

 2003-2017, GENIE - http://www.genie-mc.org

ANL_12FT,2 ANL_12FT,4 BEBC,0 BEBC,2

/ GeV] BEBC,5 BEBC,8 BNL_7FT,0 BNL_7FT,4

2 1 CCFR,2 CCFRR,0 CHARM,0 CHARM,4 FNAL_15FT,1 FNAL_15FT,2 Gargamelle,10 Gargamelle,12 cm IHEP_ITEP,0 IHEP_ITEP,2 IHEP_JINR,0 MINOS,0 SKAT,0 SciBooNE,0 -38 0.5 trunk:G00_00a:numu_freenuc

trunk:G16_02a:numu_freenuc CC [10 µ

ν 0 −1 2 10 1 10 10 Eν [GeV]

Functions of Eν Ignore nuclear effects Not ﬂux-integrated Poor statistical interpretation “Only” statistical errors Poor model discrimination power

4/49 Sometimes incomplete Helped the development of new models 2p/2h

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Dataset Evolving datasets - Present datasets

Functions of experimental Cosθµ ∈[0.9; 1 ] observables ﬂux-integrated

/GeV/n] Phys. Rev. D81, 092005 (2010) Usually differential cross-sections 2 1D, 2D cm -38 2

Organized by topology, not [10 µ

process T ∂ / µ

Higher statistics θ Cos

More statistically robust ∂ 1 )/ ⇒ Fermilab Neutrino seminar by π Mikael Kuusela - 2017/04/13 CC 0 µ ν ( σ 2 ∂ 0 0.5 1 1.5 2

miniboone_nuccqe_2010 Tµ [GeV]

5/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Dataset Evolving datasets - Present datasets

Functions of experimental Cosθµ ∈[0.9; 1 ] observables ﬂux-integrated

/GeV/n] Phys. Rev. D81, 092005 (2010) Usually differential cross-sections 2 1D, 2D cm -38 2

Organized by topology, not [10 µ

process T ∂ / µ

Higher statistics θ Cos

More statistically robust ∂ 1 )/ ⇒ Fermilab Neutrino seminar by π Mikael Kuusela - 2017/04/13 CC 0 µ ν ( σ

Sometimes incomplete 2 ∂ 0 Helped the development of new 0.5 1 1.5 2 models 2p/2h miniboone_nuccqe_2010 Tµ [GeV]

5/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Dataset Future of datasets - a personal view

One big covariance matrix per experiment ? Correlation between datasets Differential cross sections, dim > 2

No data releases with this format in SBND we are thinking about a solution ? It is usually a big effort but ...

We ﬁnally have a way to use these datsets Statistically coherent Complete error analysis

6/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE - www.genie-mc.org

GENIE Collaboration Luis Alvarez Ruso8, Costas Andreopoulos2,5, Chris Barry2, Francis Bench2, Steve Dennis2, Steve Dytman3, Hugh Gallagher7, Tomasz Golan1,4, Robert Hatcher1, Libo Jiang3, Rhiannon Jones2, Anselmo Meregaglia6, Donna Naples3, Gabriel Perdue1, Marco Roda2, Jeremy Wolcott7, Julia Yarba1

[ Faculty, Postdocs, PhD students]

1 - Fermi National Accelerator Laboratory, 2 - University of Liverpool, 3 - University of Pittsburgh, 4 - University of Wroclaw, 5 - STFC Rutherford Appleton Laboratory, 6 - IPHC Strasbourg, 7 - Tufts University, 8 - Valencia University

Core GENIE mission 1 ... provide a state-of-the-art neutrino MC generator for the world experimental neutrino community

2 ... simulate all processes for all neutrino species and nuclear targets, from MeV to PeV energy scales

3 ... perform global ﬁts to neutrino, charged-lepton and hadron scattering data and provide global neutrino interaction model tunes

7/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE status and prospects GENIE Version 2.12.6

RES CCQE models Rein-Sehgal Llewellyn Smith Berger-Sehgal Nieves, Amaro and Kuzmin-Lyubushkin- Valverde Naumov

MEC models COH Empirical Rein-Sehgal Nieves Simo Vacas Berger-Sehgal Alvarez Ruso Nuclear Models Relativistic Fermi Gas FSI - Intranuke Local Fermi Gas Single Kaon Full Intra-Nuclear Effective Spectral cascade Functions Λ production Schematic based on Hadron-nucleus data

Only one Comprehensive Model Conﬁguration (CMC) Default tune has not changed

8/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE status and prospects GENIE Version 3

“Comprehensive Model Conﬁgurations” Self-consistent collections of primary process models Tune names are supposed to become commonly used Help cooperation between collaborations

single command-line ﬂag --tune G16_02a

Complete characterization against public data Willing to host conﬁguration provided by experiments

Tunes for each CMC will also be available

A step closer toward GENIE core mission

graphics by [email protected] 9/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE status and prospects Comprehensive Model Conﬁgurations

Dedicated web page

List of available conﬁgurations

10/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

GENIE status and prospects Comprehensive Model Conﬁgurations

Details and conﬁguration

11/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Models Comprehensive Model Conﬁgurations

Conﬁgurations of interest for this talk G00_00a - Default No MEC CCQE process is Llewellyn Smith Model Dipole Axial Form Factor - Depending on MA = 0.99 GeV Nuclear model: Fermi Gas Model - Bodek, Ritchie

G16_01b - Default + MEC with Empirical MEC CCQE process is Llewellyn Smith Model Dipole Axial Form Factor - Depending on MA = 0.99 GeV Nuclear model: Fermi Gas Model - Bodek, Ritchie

G16_02a - Nieves, Simo, Vacas Model Theory motivated MEC CCQE process is Nieves Dipole Axial Form Factor - Depending on MA = 0.99 GeV Nuclear model: Local Fermi Gas Model

Small variations changing FSI models Variation including Spectral Functions

12/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Comparisons The Comparisons

The GENIE suite contains a package devoted to comparing GENIE predictions against publicly released datasets. Provides the opportunity to improve and develop GENIE models

Crucial database for new GENIE global ﬁt to neutrino scattering data

All sorts of possible formats and dimensions

Can store correlations, even between different datasets

13/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Comparisons The database

Modern Neutrino Cross Section measurement nuclear targets typically ﬂux-integrated differential cross-sections MiniBooNE, T2K, MINERvA

Historical Neutrino Cross Section Measurement Bubble chamber experiment

Measurements of neutrino-induced hadronic system characteristics Forward/backward hadronic multiplicity distributions Multiplicity correlations ... Measurements of hadron-nucleon and hadron-nucleus event characteristics (for FSI tuning) For pion, Kaons, nucleons and several nuclear targets Spanning hadron kinetic energies from few tens MeV to few GeV

Semi-inclusive electron scattering data electron-nucleus QE data electron-proton resonance data

14/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

CC 0π datasets MiniBooNE CCQE

 2003-2017, GENIE - http://www.genie-mc.org Both ν and ν¯ 2 [GeV]

Phys. Rev. D81, 092005 (2010) µ T

Phys. Rev. D88, 032001 (2013) 2 1.5 Double differential cross section

ﬂux integrated 1 1 No correlations Preferred model is Nieves Model 0.5 (G16_02a) 0 excellent agreement for ν −1 −0.5 0 0.5 1 2 χ = 101/137 DoF Cosθµ

2 -38 2 ∂ σ(νµ CC 0π)/∂ Cosθµ/∂ Tµ [10 cm /GeV/n] worse for ν¯ Data: miniboone_nuccqe_2010 χ2 = 176/78 DoF

15/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

CC 0π datasets MiniBooNE CCQE

Tµ ∈[0.4; 0.5 ] GeV

Both ν and ν¯ 2 /GeV/n] Phys. Rev. D81, 092005 (2010) 2 cm

Phys. Rev. D88, 032001 (2013) -38 1.5 [10 µ T ∂ /

Double differential cross section µ θ 1

ﬂux integrated Cos ∂ )/ π 0.5

No correlations CC 0 µ ν ( σ 2

Preferred model is Nieves Model ∂ 0 (G16_02a) −1 −0.5 0 0.5 1 ν excellent agreement for Cosθ χ2 = 101/137 DoF µ miniboone_nuccqe_2010

worse for ν¯ G00_00a χ2 = 40.6/17 DoF 2 χ = / 2 176 78 DoF G16_01b χ = 64.8/17 DoF

G16_02a χ2 = 14.3/17 DoF

15/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

CC 0π datasets T2K ND280 0π

 2003-2017, GENIE - http://www.genie-mc.org [GeV]

µ 6

Double differential cross section P 1 ﬂux integrated Phys. Rev. D93 112012 (2016)

Fully correlated 4

0.5 Tensions between datasets Preferred model is G16_01b 2 χ2 = 135/67 DoF

0 0 all models look reasonable "By eye" −1 −0.5 0 0.5 1

estimation Cosθµ correlation is complicated 2 -38 2 ∂ σ/∂ Cosθµ/∂ Pµ [10 cm /GeV/n] We can’t ignore it! Data: t2k_nd280_numucc0pi_2015

16/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

CC 0π datasets T2K ND280 0π

Cosθµ ∈[0.9; 0.94 ]

1 /GeV/n] 2

Double differential cross section cm -38

ﬂux integrated [10 µ P ∂ / µ Fully correlated θ 0.5 Cos ∂ / σ 2 Tensions between datasets ∂ Preferred model is G16_01b 2 χ = 135/67 DoF 0 0 1 2

all models look reasonable "By eye" Pµ [GeV] estimation t2k_nd280_numucc0pi_2015 correlation is complicated We can’t ignore it! G00_00a χ 2 = 17.2/8 DoF

G16_01b χ 2 = 7.75/8 DoF

G16_02a χ 2 = 25.8/8 DoF

16/49 Requirements granted by the comparisons: Data Metric

Minimizer ? Old problem in High Energy Physics CPU demanding Solution found in the Professor suite http://professor.hepforge.org Numerical assistant Developed for ATLAS experiment

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Tuning

Why tuning? Constraint parameters Provide speciﬁc tuning for experiments Liquid Argon tuning

Expected Output: Best parameters Parameter covariance matrix To be used for prior constructions

17/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Tuning

Why tuning? Constraint parameters Provide speciﬁc tuning for experiments Liquid Argon tuning

Expected Output: Best parameters Parameter covariance matrix To be used for prior constructions

Requirements granted by the comparisons: Data Metric

Minimizer ? Old problem in High Energy Physics CPU demanding Solution found in the Professor suite http://professor.hepforge.org Numerical assistant Developed for ATLAS experiment 17/49 1 Select points of param space 2 Evaluate bin’s behaviour with brute force 3 Parametrization I(p) Repeat for each bin

a parameterization Ij (p) for each bin N dimension polynomial Including all the correlation terms up to the order of the polynomial

Minimize according to~I(p)

∼ 15 parameters

Special thanks to H. Schulz

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Professor

Parametrization instead of a full MC

18/49 2 Evaluate bin’s behaviour with brute force 3 Parametrization I(p) Repeat for each bin

a parameterization Ij (p) for each bin N dimension polynomial Including all the correlation terms up to the order of the polynomial

Minimize according to~I(p)

∼ 15 parameters

Special thanks to H. Schulz

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Professor

Parametrization instead of a full MC 1 Select points of param space

18/49 3 Parametrization I(p) Repeat for each bin

a parameterization Ij (p) for each bin N dimension polynomial Including all the correlation terms up to the order of the polynomial

Minimize according to~I(p)

∼ 15 parameters

Special thanks to H. Schulz

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Professor

Parametrization instead of a full MC 1 Select points of param space 2 Evaluate bin’s behaviour with brute force

18/49 Repeat for each bin

a parameterization Ij (p) for each bin N dimension polynomial Including all the correlation terms up to the order of the polynomial

Minimize according to~I(p)

∼ 15 parameters

Special thanks to H. Schulz

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Professor

Parametrization instead of a full MC 1 Select points of param space 2 Evaluate bin’s behaviour with brute force 3 Parametrization I(p) interpolated value

18/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Professor

Parametrization instead of a full MC 1 Select points of param space 2 Evaluate bin’s behaviour with brute force 3 Parametrization I(p) Repeat for each bin interpolated value

a parameterization Ij (p) for each bin N dimension polynomial Including all the correlation terms up to the order of the polynomial

Minimize according to~I(p) p

∼ 15 parameters

Special thanks to H. Schulz

18/49 Advanced system Take into account correlations weights speciﬁc for each bin and/or dataset Proper treatment while handling multiple datasets Restrict the ﬁt to particular subsets

Priors can be included Avoid unphysical result

Nuisance rescaling parameters can be inserted proper treatment for datasets without correlations (MiniBooNE)

Reliable minimization algorithm based on Minuit

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Advantages

Highly parallelizable independent from the minimization

All kind of parameters can be tuned Not only reweight-able

19/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Professor Advantages

Highly parallelizable independent from the minimization

All kind of parameters can be tuned Not only reweight-able

Advanced system Take into account correlations weights speciﬁc for each bin and/or dataset Proper treatment while handling multiple datasets Restrict the ﬁt to particular subsets

Priors can be included Avoid unphysical result

Nuisance rescaling parameters can be inserted proper treatment for datasets without correlations (MiniBooNE)

Reliable minimization algorithm based on Minuit

19/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

The ﬁrst tuning

Tuning against CC 0π datasets

20/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Inputs Datasets - 311 data points

MiniBooNE νµ CCQE 2D histogram 137 points Data No correlation matrix 2.2

MiniBooNE ν¯µ CCQE 1.8 2D histogram 1.6 1.4

78 points 1.2 No correlation matrix 1 0.8

T2K ND280 0π (2016) V2 0.6 2D histogram 0.4 0.2

80 points 0 full covariance matrix

MINERvA νµ CCQE 1D histogram Missing Covariance between 8 points Neutrino and antineutrino data full covariance matrix Minerva released this information! MINERvA ν¯µ CCQE 1D histogram 8 points full covariance matrix

21/49 Parameter Range Default value 2 QEL-MA (GeV/c ) [0.7 ; 1.8] 0.99 QEL-CC-XSecScale [0.8 ; 1.2] 1 RES-CC-XSecScale [0.5 ; 1.5] 1 FSI-PionMFP-Scale [0.6 ; 1.4] 1 FSI-PionAbs-Scale [0.4 ; 1.6] 1 MEC-FracCCQE - G16_01b only [0 ; 1] 0.45 MEC-CC-XSecScale - G16_02a only [0.7; 1.3] 1

Other inputs: Nuisance scaling parameters 30 % for MiniBooNE Dataset Priors on QEL-CC-XSecScale and RES-CC-XSecScale Gaussian with sigma 0.1

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Inputs Models and parameters

Default + Empirical MEC Full Nieves Model G16_01b in naming scheme G16_02a in naming scheme

22/49 Other inputs: Nuisance scaling parameters 30 % for MiniBooNE Dataset Priors on QEL-CC-XSecScale and RES-CC-XSecScale Gaussian with sigma 0.1

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Inputs Models and parameters

Default + Empirical MEC Full Nieves Model G16_01b in naming scheme G16_02a in naming scheme

Parameter Range Default value 2 QEL-MA (GeV/c ) [0.7 ; 1.8] 0.99 QEL-CC-XSecScale [0.8 ; 1.2] 1 RES-CC-XSecScale [0.5 ; 1.5] 1 FSI-PionMFP-Scale [0.6 ; 1.4] 1 FSI-PionAbs-Scale [0.4 ; 1.6] 1 MEC-FracCCQE - G16_01b only [0 ; 1] 0.45 MEC-CC-XSecScale - G16_02a only [0.7; 1.3] 1

22/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Inputs Models and parameters

Default + Empirical MEC Full Nieves Model G16_01b in naming scheme G16_02a in naming scheme

Other inputs: Nuisance scaling parameters 30 % for MiniBooNE Dataset Priors on QEL-CC-XSecScale and RES-CC-XSecScale Gaussian with sigma 0.1

22/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Outputs Sheer results

G16_01b - Default + MEC G16_02a - Full Nieves Model

Parameter Best ﬁt Nominal Parameter Best ﬁt Nominal 2 2 MA (GeV/c ) 1.17 ± 0.03 0.99 ± 0.01 MA (GeV/c ) 1.00 ± 0.03 0.99 ± 0.01 QEL-CC-XSecScale 0.92 ± 0.02 1 QEL-CC-XSecScale 0.91 ± 0.02 1 RES-CC-XSecScale 1.02 ± 0.07 1 RES-CC-XSecScale 1.01 ± 0.04 1 MEC-FracCCQE 0.55 ± 0.06 0.45 MEC-CC-XSecScale 1.18 ± 0.02 1 FSI-PionMFP-Scale 0.86 ± 0.04 1.0 ± 0.2 FSI-PionMFP-Scale 1.17 ± 0.04 1.0 ± 0.2 FSI-PionAbs-Scale 0.76 ± 0.09 1.0 ± 0.3 FSI-PionAbs-Scale 1.02 ± 0.09 1.0 ± 0.3

MA is reasonably low Nieve’s model is compatible with free nucleons ﬁt Precision of MA reduced ⇒ Our choice not to add a strong prior

QEL reduced by ∼ 10% MEC increased by ∼ 20% FSI parameters strongly correlated They are better constrained than the GENIE prior

23/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Agreement Agreement with respect to datasets

G16_01b - Default + MEC G16_02a - Full Nieves Model

Dataset Best ﬁt χ2 Nominal χ2 Dataset Best ﬁt χ2 Nominal χ2 Miniboone νµ CC 0π 177 / 137 441 / 137 Miniboone νµ CC 0π 89.3 / 137 101 / 137 MiniBooNE ν¯µ CC 0π 66.2 / 78 50.4 / 78 MiniBooNE ν¯µ CC 0π 48.1 / 78 176 / 78 T2K ND 280 CC 0π 94 / 80 56.6 / 80 T2K ND 280 CC 0π 102 / 80 98.9 / 80 Total 337 / 289 548 / 295 Total 239 / 289 376 / 295

Improvement possible for both models ⇒ The ﬁt is working

Fit driven by MiniBooNE datasets Lowest information ⇒ No correlations Room for improvement

These T2K and Minerva datasets cannot be ﬁt on their own They cover a small phase space region ⇒ Parameters goes to the boundaries

24/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Best ﬁt plots

Best ﬁt - G16_01b - MiniBooNE νµ CCQE

θCosµ [0.8; 0.9 ]∈ Cosθµ ∈[0.9; 1 ] /GeV/n] /GeV/n] 2 2 cm cm

-38 2 -38 2 [10 [10 µ µ T ∂ / T ∂ / µ µ θ Cos ∂ )/ π CC 0 θ Cos

∂ 1

1 )/ π CC 0 µ µ ν ( σ ν ( σ 2 2 ∂ 0 ∂ 0 0.5 1 1.5 2 0.5 1 1.5 2

Tµ [GeV] Tµ [GeV] miniboone_nuccqe_2010 miniboone_nuccqe_2010

trunk:G16_01b:miniboone_fhc χ2 = 37.3/16 DoF trunk:G16_01b:miniboone_fhc χ2 = 95.8/18 DoF

trunk:Tuned:miniboone_fhc χ2 = 7.46/16 DoF trunk:Tuned:miniboone_fhc χ2 = 11.9/18 DoF

Fit has a big impact 25/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Best ﬁt plots

Best ﬁt - G16_01b - MiniBooNE ν¯µ CCQE

Cosθµ ∈[0.6; 0.7 ] Cosθµ ∈[0.7; 0.8 ]

0.4 0.6 /GeV/n] /GeV/n] 2 2 cm cm

-38 0.3 -38

[10 [10 0.4 µ µ T T ∂ ∂ / / µ µ

θ 0.2 θ Cos Cos ∂ ∂

)/ )/ 0.2 π π 0.1 CC 0 CC 0 µ µ ν ν ( ( σ σ 2 2 ∂ 0 ∂ 0 0.5 1 1.5 2 0.5 1 1.5 2

Tµ [GeV] Tµ [GeV] miniboone_nubarccqe_2013 miniboone_nubarccqe_2013

trunk:G16_01b:miniboone_rhc χ2 = 6.28/8 DoF trunk:G16_01b:miniboone_rhc χ2 = 9.62/9 DoF

trunk:Tuned:miniboone_rhc χ2 = 6.1/8 DoF trunk:Tuned:miniboone_rhc χ2 = 11.8/9 DoF

Improvement not really necessary in this case 26/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Best ﬁt plots Best ﬁt - G16_01b - MINERvA Neutrinos Antineutrinos

 2003-2017, GENIE - http://www.genie-mc.org  2003-2017, GENIE - http://www.genie-mc.org 1.5 PRL 111, 022501 (2013) 1.5 PRL 111, 022502 (2013) /proton] 2 /neutron] 2 1 /GeV 2

/GeV 1 2 cm cm -38

-38 0.5 0.5 [10 [10 2 QE 2 QE /dQ σ /dQ d σ 0 0 d 0 0.5 1 1.5 2 0 0.5 1 1.5 2 Q2 [GeV2] Q2 [GeV2] MINERvAExDataCCQEQ2 QE MINERvAExDataCCQEQ2 QE

trunk:G16_01b:minerva_numu_2013 χ2 = 17.5/8 DoF trunk:G16_01b:numubar_2013 χ2 = 6.23/8 DoF

trunk:Tuned:minerva_numu_2013 χ2 = 10.9/8 DoF trunk:Tuned:numubar_2013 χ2 = 4.7/8 DoF

⇒ "Eye evaluation" wouldn’t prefer a model over the other

27/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Best ﬁt plots Best ﬁt - G16_01b - T2K ND280

Pµ ∈[0.8; 0.95 ] GeV

0.8 /GeV/n] 2 cm -38 0.6 [10 µ P ∂

agreement with T2K has worsened / µ θ 0.4

not surprising Cos ∂ / σ 2

⇒ Tensions already highlighted ∂ 0.2 χ2: 57 → 94 / 80 DoF 0 0.6 0.7 0.8 0.9 1

t2k_nd280_numucc0pi_2015_rps Cosθµ

trunk:G16_01b:t2k_nd280_numu_fhc χ2 = 6.9/8 DoF

trunk:Tuned:t2k_nd280_numu_fhc χ2 = 9.32/8 DoF

28/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Best ﬁt plots Best ﬁt - G16_02a

Cosθµ ∈[0.9; 1 ] /GeV/n] 2

cm 1 -38 [10 µ T ∂ /

Nieves’ model already works well µ θ Agreement is preserved Cos 0.5 ∂ )/ π Notable improvement only w.r.t. CC 0

MiniBooNE ν¯µ µ ν ( σ 2 ∂ 0 0.5 1 1.5 2

Tµ [GeV] miniboone_nubarccqe_2013

trunk:G16_02a:miniboone_rhc χ2 = 53.8/15 DoF

trunk:G16_02a_tuned:miniboone_rhc χ2 = 13.5/15 DoF

29/49 Data from Liquid argon experiments Part of GENIE collaboration is in SBND Plan for argon tunings

Look forward to more data

Release these results Paper is in preparation Implementation in GENIE v3

Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Tuning program Next steps

More tunings can be done hadronization retune Pythia 6 and 8 (implementation is ongoing) Tune of FSI Both hN and hA intranuke Free nucleon cross section model including dσ/dQ2 data

30/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Tuning program Next steps

More tunings can be done hadronization retune Pythia 6 and 8 (implementation is ongoing) Tune of FSI Both hN and hA intranuke Free nucleon cross section model including dσ/dQ2 data

Data from Liquid argon experiments Part of GENIE collaboration is in SBND Plan for argon tunings

Look forward to more data

Release these results Paper is in preparation Implementation in GENIE v3

30/49 Introduction GENIE Comparisons Tuning CC 0π tuning Conclusion

Conclusions Conclusion

We are renewing GENIE New models Systematic validation against Cross section data Maintained and rich database

We have a very powerful ﬁtting machinery Validated A new branch of analyses Alternative tool to propagate systematic uncertainties

What we can do depends on data quality Look forward a promising collaboration between generators, experiments and theorists

31/49 Backup

Backup slides

32/49 Backup

Parametrization residuals

/global 350000

50000 300000

250000 40000

200000 30000

150000 20000 100000

10000 50000

0 0 0.4 0.2 0.0 0.2 0.4 0.5 0.0 0.5 Good Bad

33/49 Backup

Data covariance

Data Covariance

0.2

bin 300

250 0.15

200

0.1 150

100 0.05

50 0

0 0 50 100 150 200 250 300 bin

34/49 Backup

Model dependencies Model dependencies in Oscillation Analyses

A simple ratio between Near and Far spectra is not enough Detectors exposed to different ﬂux “functionally identical” detectors do not exists

Near ﬂux has to be ﬁtted at the near detector and then propagated ⇒ Models required

35/49 Backup

Model dependencies Model dependencies in Oscillation Analyses (2)

2 2 2 CCQE is a 2-body reaction mp − (mn − Eb) − m` + 2(mn − Eb)E` Eν = Eν depends is just a function of 2(mn − Eb − E` + p`cosθ`) lepton momentum and angle

2p/2h is not a 2-body reaction low energy tails in reconstructed energy distributions

2p/2h also relevant for CP searches np-nh is different for ν/ν¯

⇒ 2p/2h modelling is important to achieve required precision Martini et al.

36/49 Backup

Model Comparisons Model comparison

37/49 Backup

Model Comparisons Model comparison

[M.Martini, FUNFACT J Lab workshop]

38/49 Backup

Importance of Covariance Importance of the covariance - an example

 2003-2017, GENIE - http://www.genie-mc.org

Real dataset 1.5 8 points /neutron] 2 Which is the best agreeing curve?

/GeV 1 Black 2 Red cm

-38 Difference in terms of sigma? 0.5 [10 < 1 >

2 QE 1

2 /dQ Black χ = 17.5/8 DoF σ 0 d 0 0.5 1 1.5 2 Red χ2 = 10.9/8 DoF 2 2 ⇒ Almost 2 σ QQE [GeV ]

39/49 Not a full covariance matrix Neglecting the same ﬂux Same detector/reconstruction

We can check agreement we can not ﬁt these data without neglecting a correlation

Backup

Importance of Covariance Example of easy improvement

arXiv:1705.03791v1 Minerva experiment -39 - νµ C → µ p 16 ×10 Cross sections of CC 1-proton on 3.06e+20 Data POT Data 14 GENIE with FSI different targets GENIE No FSI 12 NuWro with FSI NuWro No FSI

C, Fe, Pb /nucleon) 2 10

/GeV 8 2 Wonderful dataset 6 ( cm 2 2p/2h and FSI tuning 4 /dQ C σ

d 2 0 Covariance matrices for each target 0 0.2 0.4 0.6 0.8 1 1.2 1.4 2 2 Qp(GeV )

Best format among present data releases -39 - νµ Pb → µ p 16 ×10 3.06e+20 Data POT Data 14 GENIE with FSI GENIE No FSI 12 NuWro with FSI

/nucleon) NuWro No FSI 2 10 /GeV

2 8

( cm 6 2 4 /dQ Pb

σ 2 d 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 2 2 Qp(GeV )

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Importance of Covariance Example of easy improvement

C, Fe, Pb /nucleon) 2 10

/GeV 8 2 Wonderful dataset 6 ( cm 2 2p/2h and FSI tuning 4 /dQ C σ

d 2 0 Covariance matrices for each target 0 0.2 0.4 0.6 0.8 1 1.2 1.4 2 2 Qp(GeV )

Best format among present data releases -39 - νµ Pb → µ p 16 ×10 3.06e+20 Data POT Data 14 GENIE with FSI Not a full covariance matrix GENIE No FSI 12 NuWro with FSI

/nucleon) NuWro No FSI Neglecting the same ﬂux 2 10

Same detector/reconstruction /GeV

2 8

( cm 6 2 4 We can check agreement /dQ Pb

σ 2 we can not ﬁt these data d 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 2 2 without neglecting a correlation Qp(GeV )

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0π puzzle CC Quasi-Elastic - 0π on single nucleons

Theoretically well understood One diagram μ N’

A, B and C are form factors They have to be measured W+ B and C are known from e-N Ʋ scattering N A to be extracted from ν data

Axial Form factor Dipole standard g = 1.26 from neutron β decay parameterization A −2 ﬁtted based on ∂σ/∂Q2 data 2 Q2 A(Q ) = gA 1 + 2 MA

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0π puzzle CC Quasi-Elastic - Data

 2003-2017, GENIE - http://www.genie-mc.org ] 2 2 cm -38 Hydrogen / Deuterium data 1.5 from 0.1 GeV to ∼ 100 GeV 1 For both Neutrinos and CCQE [10 0.5 Anti-neutrinos µ ν 0 Critical parameter: MA 10−1 1 10 M ∼ 1 GeV A ANL_12FT,1 ANL_12FT,3 Eν [GeV] BEBC,12 BNL_7FT,3 FNAL_15FT,3 Gargamelle,2 NOMAD,2 SERP_A1,0 SERP_A1,1 SKAT,8

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0π puzzle CC Quasi-Elastic - Data ) 2 2.4 ν n→µ − p / GeV 2.2 µ 2

cm 2 − 38 1.8 Allasia et al., CERN WA25 (BEBC) 1990 (10 Target: D2 (converted to free neutron) >

Hydrogen / Deuterium data 2 1.6 Event number: 552 5 ≈ 54 GeV from 0.1 GeV to ∼ 100 GeV 1.4 ν ν MA = 0.999 ± 0.011 GeV(global fit)

For both Neutrinos and < d σ / dQ 1.2 MA = 0.890 ± 0.044 GeV(best fit) Anti-neutrinos 1

0.8 Critical parameter: MA 0.6 MA ∼ 1 GeV 0.4

0.2

0 0 0.5 1 1.5 2 2.5 3 3.5 Q2 (GeV2)

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0π puzzle 0π on heavy nuclei

MiniBooNE data

AIP Conf. Proc. 1189: 139-144 (2009); Phys. Rev. D 81, 092005 (2010)

On heavy nuclei things got complicated MiniBooNE ⇒ ﬁrst evidence Carbon target

Possible explanation from enhanced MA ⇒ incompatibility with "historical" datasets

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0π puzzle 0π on heavy nuclei - Solution

MoniBooNE is Cherenkov 2p-2h scheme detector Not able to see nucleons

miniBooNE dataset is a CCQE-like sample

genuine CCQE Multinucleon Emission np-nh Leading contribution is 2p-2h (2 particles - 2 holes)

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0π puzzle 2 Particles - 2 Holes

M. Martini NN correlations MEC NN correlation-MEC interference

16 diagrams 49 diagrams 56 diagrams

Not easy to have a complete model Different approaches include different diagrams

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Data from comparisons Hadronization example

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Data from comparisons Hadronization example

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Data from comparisons Hadronization example

> 4 0 π

2 FNAL 15’ Ne+H2ν

BEBC Ne+H2ν

BEBC H2ν p G00_00aν 1 n G00_00aν p G16_02aν n G16_02aν 0 1 10 102 103 W2(GeV2/c4)

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Data Constraints for Oscillation analyses

Propagate parameters uncertainty through the parameterization

Muon Angle for 0π events 3 ×10 450

Events 400

350 300 Default 250

200 Tuned

150

100

0 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Cos θ

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Systematic propagation Tuning Output

Parameters best ﬁt Parameters covariance

Prediction covariance due to the propagation of parameter covariance

49/49 Propagate the result to other observables

Propagate parameters uncertainty through the parameterization

Muon Angle for 0π events 3 ×10 450

Events 400

350 300 Default 250

200 Tuned

150

100

0 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Cos θ

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Systematic propagation Tuning Output

Data Constraints for Oscillation Parameters best ﬁt analyses Parameters covariance

Prediction covariance due to the propagation of parameter covariance

Muon Angle for 0π events 3 ×10 450

Events 400

350

300 Default

250

200

150

100

0 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Cos θ

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Backup

Systematic propagation Tuning Output

Data Constraints for Oscillation Parameters best ﬁt analyses Parameters covariance Propagate the result to other observables Prediction covariance due to the propagation of parameter covariance

Muon Angle for 0π events Muon Angle for 0π events 3 3 ×10 ×10 450 450 Events Events 400 400

350 350 300 Default 300 Default 250 250 Tuned 200 200

150 150

100 100

50 50

0 0 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Cos θ Cos θ

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Systematic propagation Tuning Output

Data Constraints for Oscillation Parameters best ﬁt analyses Parameters covariance Propagate the result to other observables Prediction covariance due to the propagation of parameter Propagate parameters covariance uncertainty through the parameterization

Muon Angle for 0π events Correlation 3 ×10 450 1

45 0.8 Events 400 bin 40 0.6 350

35 0.4 300

30 0.2 Default 250 25 0 200 Tuned 20 −0.2 150 15 −0.4 100 10 −0.6 50 5 −0.8

0 0 −1 0 5 10 15 20 25 30 35 40 45 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 bin Cos θ

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