Exclusive VM electroproduction
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Transcript of Exclusive VM electroproduction
June 10, 2008 A. Levy: Exclusive VM, GPD08, Trento 1
Exclusive VM electroproduction
Aharon LevyTel Aviv University
on behalf of the H1 and ZEUS collaborations
* 0
0 , , , / ,
p V p
V J
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Why are we measuring
IP
‘soft’
WW )(
‘hard’
gg||tbe
dt
d
• Expect to increase from soft (~0.2, from ‘soft Pomeron’ value) to hard (~0.8, from xg(x,Q2)2)
• Expect b to decrease from soft (~10 GeV-2)
to hard (~4-5 GeV-2)
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s0.096
2( )2
QF x
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Why are we measuring
IP
‘soft’
WW )(
‘hard’
gg||tbe
dt
d
• Expect to increase from soft (~0.2, from ‘soft Pomeron’ value) to hard (~0.8, from xg(x,Q2)2)
• Expect b to decrease from soft (~10 GeV-2)
to hard (~4-5 GeV-2)
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Why are we measuring
Use QED for photon wave function. Study properties of VM wf and the gluon density in the proton.
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Mass distributions
KK /J
PHP
DIS 0
0
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Photoproduction
W process becomes hard as scale (mass) becomes larger. real part of amplitude increases with hardness.
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(W) – ρ0
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Proton dissociationMC: PYTHIA
pdiss: 19 ± 2(st) ± 3(sys) %
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(W) – ρ0,
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(W) - , J/,
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(Q2+M2) - VM
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Kroll + Goloskokov: = 0.4 + 0.24 ln (Q2/4) (close to the CTEQ6M gluon density, if parametrized as xg(x)~x-/4)
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(Q2) nMQ
22
Fit to whole Q2 range gives bad 2/df (~70)
VM n comments
ρ 2.44±0.09 Q2>10 GeV2
2.75±0.13 ±0.07
Q2>10 GeV2
J/ 2.486±0.080±0.068
All Q2
1.54±0.09 ±0.04
Q2>3 GeV2
101Q2(GeV2)
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(Q2)
* 2
2
2 2 ( )
1( )
( )p n QQ
Q M
2 2( ) 2.15 0.007n Q Q
(for Q2 > 1 GeV2)
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Proton vertex factorisation
photoproduction
proton vertex factorisation
Similar ratio within errors for and
proton vertex factorisation in DIS
IP IP
Yelastic p-dissociative
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b(Q2) – ρ0, Fit
||tbedt
d
:
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b(Q2+M2) - VM
2 2( )r b c
‘hard’
gg
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Information on L and T
Use 0 decay angular distribution to get r0400 density matrix
element 04 04 200 00(cos ) (1 ) (3 1)cosh hf r r
04000400
1
1L
T
rR
r
- ratio of longitudinal- to transverse-photon fluxes ( <> = 0.996)
0400
L L
L T tot
r
using SCHC
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R=L/T (Q2)When r00
04 close to 1, error on R large and asymmetric advantageous to use r00
04 rather than R.
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R=L/T (Q2)
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R=L/T (Mππ)
Why??
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R=L/T (Mππ)
Possible explanation:
2
1R
M
example for =1.5
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Photon configuration - sizessmall kT large
kTlarge config.
small config.
T: large size small size
strong color forces color screening
large cross section small cross section
*: *T, *L
*T – both sizes, *L – small size
Light VM: transverse size of ~ size of proton
Heavy VM: size small cross section much smaller (color transparency) but due to small size (scale given by mass of VM) ‘see’ gluons in the proton ~ (xg)2 large
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L/tot(W)
L and T same W dependence
L in small configuration
T in small and large configurations
small configuration steep W dep
large configuration slow W dep
large configuration is suppressed
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L/tot(t)
TL bb
size of *L *T
large configuration suppressed
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(W) - DVCS* p p
Final state is real T
using SCHC initial * is *T
but W dep of steep
large *T configurations suppressed
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Effective Pomeron trajectory
ρ0
photoproduction
Get effective Pomeron trajectory from d/dt(W) at fixed t
2[2 ( ) 2]( ) ( ) IP td
W F t Wdt
Regge:
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Effective Pomeron trajectory
ρ0 electroproduction
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Comparison to theory
• All theories use dipole picture
• Use QED for photon wave function
• Use models for VM wave function – usually take a Gaussian shape
• Use gluon density in the proton
• Some use saturation model, others take sum of nonperturbative + pQCD calculation, and some just start at higher Q2
• Most work in configuration space, MRT works in momentum space. Configuration space – puts emphasis on VM wave function. Momentum space – on the gluon distribution.
• W dependence – information on the gluon
• Q2 and R – properties of the wave function
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ρ0 data (ZEUS) - Comparison to theory
• Martin-Ryskin-Teubner (MRT) – work in momentum space, use parton-hadron duality, put emphasis on gluon density determination. Phys. Rev. D 62, 014022 (2000).
• Forshaw-Sandapen-Shaw (FSS) – improved understanding of VM wf. Try Gaussian and DGKP (2-dim Gaussian with light-cone variables). Phys. Rev. D 69, 094013 (2004).
• Kowalski-Motyka-Watt (KMW) – add impact parameter dependence, Q2 evolution – DGLAP. Phys. Rev. D 74, 074016 (2006).
• Dosch-Ferreira (DF) – focusing on the dipole cross section using Wilson loops. Use soft+hard Pomeron for an effective evolution. Eur. Phys. J. C 51, 83 (2007).
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ρ0 data (H1) - Comparison to theory
• Marquet-Peschanski-Soyez (MPS): Dipole cross section from fit to previous , and J/ data. Geometric scaling extended to non-forward amplitude. Saturation scale is t-dependent.
• Ivanov-Nikolaev-Savin (INS): Dipole cross section obtained from BFKL Pomeron. Use kt-unintegrated PDF and off-forward factor.
• Goloskokov-Kroll (GK): Factorisation of hard process and proton GPD. GPD constructed from standard PDF with skewing profile function.
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Q2
KMW – good for Q2>2GeV2 miss Q2=0
DF – miss most Q2
FSS – Gauss better than DGKP
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Q2
Data seem to prefer
MRST99 and CTEQ6.5M
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W dependence
KMW - close
FSS:
Sat-Gauss – right W-dep.
wrong norm.
MRT:
CTEQ6.5M – slightly better in W-dep.
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L/tot(Q2)
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L/tot(W)
All models have mild W dependence. None describes all kinematic regions.
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L,T (Q2+M2)
• Different Q2+M2 dependence of L and T (L0 at Q2=0)
• Best description of L by GK; T not described.
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Density matrix elements - ,
• Fair description by GK
• r500 violates SCHC
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VM/tot
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Summary and conclusions• New high statistics measurements on ρ0,
electroproduction and on DVCS.• The Q2 dependence of (*pρp) cannot be described by a
simple propagator term.• The cross section is rising with W and its logarithmic
derivative in W increases with Q2.• The exponential slope of the t distribution decreases with Q2
and levels off at about b = 5 GeV-2.• Proton vertex factorisation observed also in DIS.• The ratio of cross sections induced by longitudinally and
transversely polarised virtual photons increases with Q2, but is independent of W and t.
• The effective Pomeron trajectory has a larger intercept and smaller slope than those extracted from soft interactions.
• All these features are compatible with expectations of perturbative QCD.
• None of the models which have been compared to the measurements are able to reproduce all the features of the data.