prospects on nucleon tomography - inpc · 2019-09-01 · neural network fits. framework. design...
TRANSCRIPT
Prospects on nucleon tomography
INPC | Hervé MOUTARDE
Jul. 30, 2019
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NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Imaging the origin of mass.Identification of underlying mechanisms from parton distributions.
How can we recover the well-known characterics of thenucleon from the properties ofits colored building blocks?
Mass?Spin?Charge?…
What are the relevant effec-tive degrees of freedom andeffective interaction at largedistance?
H. Moutarde INPC 2019 2 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Imaging the origin of mass.Identification of underlying mechanisms from parton distributions.
How can we recover the well-known characterics of thenucleon from the properties ofits colored building blocks?
Mass?Spin?Charge?…
What are the relevant effec-tive degrees of freedom andeffective interaction at largedistance?
H. Moutarde INPC 2019 2 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Experimental access to geometrical information.
Colorless proton ??↔
Colorful components
”Can one hear the shape of a drum?”
Kac, Am. Math. Mon. 74, 1 (1966)
In quantitative terms
∂ΩΩ
Dirichlet problem for the Laplacian:
∆u + λu = 0 and u|∂Ω = 0
H. Moutarde INPC 2019 3 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Experimental access to geometrical information.
”Can one hear the shape of a drum?”
Kac, Am. Math. Mon. 74, 1 (1966)
In quantitative terms
∂ΩΩ
Dirichlet problem for the Laplacian:
∆u + λu = 0 and u|∂Ω = 0
H. Moutarde INPC 2019 3 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Experimental access to geometrical information.
”Can one hear the shape of a drum?”
Kac, Am. Math. Mon. 74, 1 (1966)
In quantitative terms
∂ΩΩ
Dirichlet problem for the Laplacian:
∆u + λu = 0 and u|∂Ω = 0
H. Moutarde INPC 2019 3 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Experimental access to geometrical information.
”Can one hear the shape of a drum?”
Kac, Am. Math. Mon. 74, 1 (1966)
In quantitative terms
∂ΩΩ
Dirichlet problem for the Laplacian:
∆u + λu = 0 and u|∂Ω = 0
H. Moutarde INPC 2019 3 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Harmonics and patterns.
Vibration patterns Vibration patterns
What about the proton?”Hit” the proton, e.g. with a virtual photon:”Listen” to the distribution of produced particles:”Measure” harmonics:
H. Moutarde INPC 2019 4 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Harmonics and patterns.
Vibration patterns Vibration patterns
What about the proton?”Hit” the proton, e.g. with a virtual photon:”Listen” to the distribution of produced particles:”Measure” harmonics:
H. Moutarde INPC 2019 4 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Can one hear the shape of a proton?Harmonics and patterns.
Vibration patterns Vibration patterns
What about the proton?”Hit” the proton, e.g. with a virtual photon: hard”Listen” to the distribution of produced particles: exclusive”Measure” harmonics: amplitudes (Compton form factors)
H. Moutarde INPC 2019 4 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorization
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorization
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorization
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
Pert
urba
tive
Non
pert
urba
tive
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorization
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
Pert
urba
tive
Non
pert
urba
tive
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorization
Non
pert
urba
tive
Pert
urba
tive
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
Non
pert
urba
tive
Pert
urba
tive
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorization
Non
pert
urba
tive
Pert
urba
tive
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes of current interest.Factorization, universality and event distributions.
e−DVCS e−
γ∗ Q2
p pt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
e−DVMP e−
γ∗ Q2
p pt
x + ξ x − ξ
factorization
π, ρ, . . .
Pert
urba
tive
Non
pert
urba
tive
TCS
e−
e+
γ∗ Q2
ppt
x + ξ x − ξ
γ
factorizationPert
urba
tive
Non
pert
urba
tive
H. Moutarde INPC 2019 5 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.Generalized Parton Distributions (GPDs) and 3D structure.
Correlation of the longitudinal momentum and thetransverse position of a parton in a hadron.DVCS recognized as the cleanest channel to access GPDs.
Deeply Virtual Compton Scattering (DVCS)
e−DVCS e−
γ∗, Q2
p p
γ
x + ξ x − ξ
factorization µF
t
R⊥
Transverse centerof momentum R⊥R⊥ =
∑i xir⊥i
24 GPDs Fi(x, ξ, t, µF) for each parton type i = g, u, d, . . .for leading and sub-leading twists.
H. Moutarde INPC 2019 6 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.Generalized Parton Distributions (GPDs) and 3D structure.
Correlation of the longitudinal momentum and thetransverse position of a parton in a hadron.DVCS recognized as the cleanest channel to access GPDs.
Deeply Virtual Compton Scattering (DVCS)
e−DVCS e−
γ∗, Q2
p p
γ
x + ξ x − ξ
factorization µF
t
R⊥
Transverse centerof momentum R⊥R⊥ =
∑i xir⊥ib⊥
Impactparameter b⊥
24 GPDs Fi(x, ξ, t, µF) for each parton type i = g, u, d, . . .for leading and sub-leading twists.
H. Moutarde INPC 2019 6 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.Generalized Parton Distributions (GPDs) and 3D structure.
Correlation of the longitudinal momentum and thetransverse position of a parton in a hadron.DVCS recognized as the cleanest channel to access GPDs.
Deeply Virtual Compton Scattering (DVCS)
e−DVCS e−
γ∗, Q2
p p
γ
x + ξ x − ξ
factorization µF
t
R⊥
Transverse centerof momentum R⊥R⊥ =
∑i xir⊥ib⊥
Impactparameter b⊥
xP+
Longitudinalmomentum xP+
24 GPDs Fi(x, ξ, t, µF) for each parton type i = g, u, d, . . .for leading and sub-leading twists.
H. Moutarde INPC 2019 6 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.Generalized Parton Distributions (GPDs) and 3D structure.
Correlation of the longitudinal momentum and thetransverse position of a parton in a hadron.DVCS recognized as the cleanest channel to access GPDs.
Deeply Virtual Compton Scattering (DVCS)
e−DVCS e−
γ∗, Q2
p p
γ
x + ξ x − ξ
factorization µF
t
R⊥
Transverse centerof momentum R⊥R⊥ =
∑i xir⊥ib⊥
Impactparameter b⊥
xP+
Longitudinalmomentum xP+
−1 < x < +1−1 < ξ < +1
24 GPDs Fi(x, ξ, t, µF) for each parton type i = g, u, d, . . .for leading and sub-leading twists.
H. Moutarde INPC 2019 6 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Exclusive processes amplitudes.The sound of DVCS: Compton Form Factors (CFFs).
Bjorken regime : large Q2 and fixed xB ≃ 2ξ/(1 + ξ)
Partonic interpretation relies on factorization theorems.All-order proofs for DVCS, TCS and some DVMP.GPDs depend on a (arbitrary) factorization scale µF.Consistency requires the study of different channels.
GPDs enter DVCS through Compton Form Factors :
F(ξ, t,Q2) =
∫ 1
−1dx C
(x, ξ, αS(µF),
QµF
)F(x, ξ, t, µF)
for a given GPD F.CFF F is a complex function.
H. Moutarde INPC 2019 7 / 22
Phenomenology
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NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.3D hadron imaging.
Probabilistic interpretation of Fourier transform ofGPD(x, ξ = 0, t) in transverse plane.
ρ(x, b⊥, λ, λN) =1
2
[H(x, 0, b2⊥) +
bj⊥ϵjiSi
⊥M
∂E∂b2⊥
(x, 0, b2⊥)
+λλNH(x, 0, b2⊥)]
Notations : quark helicity λ, nucleon longitudinalpolarization λN and nucleon transverse spin S⊥.
Burkardt, Phys. Rev. D62, 071503 (2000)Can we obtain this picture from exclusive measurements?
x ~ 0.3xx < 0.01 ~ 0.1
spintransverse
(c)
b
xP
longitud.
(a)
transverse
pioncloud quarks
valence
(b)
quarks, gluonssinglet
quarks movefaster
slower Weiss, AIP Conf.Proc. 1149,150 (2009)
H. Moutarde INPC 2019 9 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.3D hadron imaging.
Probabilistic interpretation of Fourier transform ofGPD(x, ξ = 0, t) in transverse plane.
ρ(x, b⊥, λ, λN) =1
2
[H(x, 0, b2⊥) +
bj⊥ϵjiSi
⊥M
∂E∂b2⊥
(x, 0, b2⊥)
+λλNH(x, 0, b2⊥)]
Notations : quark helicity λ, nucleon longitudinalpolarization λN and nucleon transverse spin S⊥.
Burkardt, Phys. Rev. D62, 071503 (2000)Not quite, but close!
0
0.25
0.5
u
PARTONS Fits 2018-1
10-2 10-1 100
x
-3
-2
-1
0
1
2
3
b⟂ [
fm] Moutarde et al.,
Eur. Phys. J. C78,890 (2018)
H. Moutarde INPC 2019 9 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Anatomy of hadrons.Spin and energy-momentum structure.
Energy momentum tensor between nucleon states:⟨N,P +
∆
2
∣∣∣∣Tµν
∣∣∣∣N,P − ∆
2
⟩= u
(P +
∆
2
)[A(t)γ(µPν)∆
2
+B(t)P(µiσν)λ∆λ
2M +C(t)M (∆µ∆ν −∆2ηµν)
]u(
P − ∆
2
)with t = ∆2.Key observation: link between GPDs and gravitationalform factors∫
dx xHq(x, ξ, t) = Aq(t) + 4ξ2Cq(t)∫dx xEq(x, ξ, t) = Bq(t)− 4ξ2Cq(t)
Ji, Phys. Rev. Lett. 78, 610 (1997)See C. Lorcé’s talk today!
H. Moutarde INPC 2019 10 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Need for global fits of world data.Different facilities will probe different kinematic domains.
ThomasJeffersonNational
Laboratory
DESY
CERN
Experimental data collected at3 facilities, soon 4:EIC !
H. Moutarde INPC 2019 11 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Need for global fits of world data.Different facilities will probe different kinematic domains.
ThomasJeffersonNational
Laboratory
DESY
CERN
Experimental data collected at3 facilities, soon 4:EIC !
Valence quarks
H. Moutarde INPC 2019 11 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Need for global fits of world data.Different facilities will probe different kinematic domains.
ThomasJeffersonNational
Laboratory
DESY
CERN
Experimental data collected at3 facilities, soon 4:EIC !
Valence quarks
Sea quarks
H. Moutarde INPC 2019 11 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Need for global fits of world data.Different facilities will probe different kinematic domains.
ThomasJeffersonNational
Laboratory
DESY
CERN
Experimental data collected at3 facilities, soon 4:EIC !
Valence quarks
Sea quarks
GluonsNSAC, Long Range Plan 2015:”We recommend [...] EIC as the highestpriority for new facility construction”
H. Moutarde INPC 2019 11 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Existing and future DVCS measurements.The field is changing with new data of unprecedented accuracy.
CLAS: preliminary results with 2% of approved beam time
EFF unc.
Pol. PDF unc.
PDF unc.
PARTONS Fits 2018-1
0 ¹⁄₂ π π 1¹⁄₂ π 2 πϕ [rad]
-0.5
-0.25
0
0.25
0.5
AU
L-
Moutarde et al.,Eur. Phys. J. C78, 890
(2018)Christiaens,
APS DNP’18
See the talks by J. Roche, M. Defurne, G. Christiaens,S. Niccolai, P. Chatagnon and S. Scopetta on Thursday!
H. Moutarde INPC 2019 12 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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Neural network global fit of CFFs.All existing sets except d4σ−
UU from Hall A (2015-17).
Moutarde et al., Eur. Phys. J. C79, 614 (2019)H. Moutarde INPC 2019 13 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
Releases
Conclusion
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A selection of results.2600+ measurements of 30 observables published during 2001-17.
CLASPARTONS Fits NN 2019
0 ¹⁄₂π π 1¹⁄₂πϕ [rad]
-0.5
-0.25
0
0.25
0.5AUL-
2π
Hall APARTONSFitsNN2019
0 ¹⁄₂π π 1¹⁄₂π 2πϕ[rad]
0
0.025
0.05
0.075
0.1
d⁴σ[nb/GeV⁴]
HERMESPARTONS Fits NN 2019
0 0.05 0.1 0.15 0.2 0.25xBj
-0.2
-0.1
0
0.1
0.2
ACcos0ϕ
0.3
COMPASSPARTONSFitsNN2019
0 0.2 0.4 0.6 0.8-t[GeV²]
10-2
10-1
100
101
d³σ[nb/GeV⁴]
Moutarde et al., Eur. Phys. J. C79, 614 (2019)H. Moutarde INPC 2019 14 / 22
NucleonTomography
PrincipleAnalogy
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A selection of results.2600+ measurements of 30 observables published during 2001-17.
Compton form factor ImH(ξ, t = −0.3 GeV2,Q2 = 2. GeV2)
PARTONSFitsNN2019
10-6 10-5 10-4 10-3 10-2 10-1 100ξ
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Moutarde et al., Eur. Phys. J. C79, 614 (2019)H. Moutarde INPC 2019 14 / 22
NucleonTomography
PrincipleAnalogy
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A selection of results.2600+ measurements of 30 observables published during 2001-17.
Subtraction constant (related to pressure distribution)
PARTONS Fits NN 2019
0 0.2 0.4 0.6 0.8 1-t [GeV2]
-10
-5
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CH
Moutarde et al., Eur. Phys. J. C79, 614 (2019)H. Moutarde INPC 2019 14 / 22
The PARTONS framework
PARtonicTomographyOfNucleonSoftware
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NucleonTomography
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Computing chain design.Differential studies: physical models and numerical methods.
Large distanceFirst
principles andfundamentalparameters
Small distanceComputationof amplitudes
Full processesExperimental
data andphenomenology
GPD at µ = µrefF
GPD at µrefF
DVCS
TCS
DVM
P
DVCS
TCS
DVM
P
PARtonicTomographyOfNucleonSoftware
Perturbativeapproximations.Physical models.Fits.Numericalmethods.Accuracy andspeed.
Evolution
H. Moutarde INPC 2019 16 / 22
NucleonTomography
PrincipleAnalogy
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A computing framework for physics.Done: tests, benchmarking, documentation, tutorials.
3 stages:1 Design.2 Integration and validation.3 Benchmarking and production.
1 new physical development = 1 new module.Aggregate knowledge and know-how:
Models.Measurements.Numerical techniques.Validation.
What can be automated will be automated.Flexible software architecture.
B. Berthou et al., Eur. Phys. J. C78, 478 (2018)
H. Moutarde INPC 2019 17 / 22
NucleonTomography
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Open source release.Publicly available on CEA GitLab server.
Berthou et al., Eur. Phys. J. C78, 478 (2018)H. Moutarde INPC 2019 18 / 22
NucleonTomography
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Open source release.Publicly available on CEA GitLab server.
Berthou et al., Eur. Phys. J. C78, 478 (2018)H. Moutarde INPC 2019 18 / 22
NucleonTomography
PrincipleAnalogy
Experimental access
PhenomenologyPhysical content
DVCS data
Neural network fits
FrameworkDesign
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Conclusion
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Open source release.Publicly available on CEA GitLab server.
Berthou et al., Eur. Phys. J. C78, 478 (2018)H. Moutarde INPC 2019 18 / 22
NucleonTomography
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Within the next four years.Virtual Access Infrastructure 3DPartons in STRONG-2020.
A workpackage of the EU-funded STRONG-2020 programMutualize developments for GPD and TMD frameworks.Address the question of event generation.Open-source release and maintenance of computing codesrelated to 3D hadron structure.
H. Moutarde INPC 2019 19 / 22
Conclusion
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NucleonTomography
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Conclusion and prospects.Putting all the pieces together.
We now have tools to systematically relate models toexperimental data in multi-channel analysis.We now have an operating engine for global CFF fits.We revisit the mechanical properties of hadrons toassess how much we can learn from GPD extractions.We can now build generic GPD models satisfying a prioriall theoretical constraints.
New studies become possible!Global GPD fits.Energy-momentum structure of hadrons.Quantitative impact of nonperturbative QCD ingredientson 3D hadron structure studies.GPD and TMD studies in a common framework.... And probably much more!
H. Moutarde INPC 2019 21 / 22
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