(NN)PDFs, the LHC & the LHeC - Indiana University · Monte Carlo. The PDF ﬁtting game Status of...

(NN)PDFs, the LHC & the LHeCAlberto GuffantiNiels Bohr International Academy & Discovery CenterNiels Bohr Intitute - Copenhagen

Parton Distribution FunctionsWhat are they?

Consider a process with a single hadron in the initial state

The cross section for such a process can be written (Factorization Theorem) as

dσ = dξξ0

1

∫a∑ Da (x,µ

2 )dσ axξ, sµ2 ,α s (µ

2 )⎛⎝⎜

⎞⎠⎟+O 1

Qp

⎛⎝⎜

⎞⎠⎟

Partonic Cross Section

Parton Distribution Function

Parton Distribution FunctionsWhat are they?

• Parton Distribution Functions are non-perturbative objects and their value at given x and Q2 cannot be computed in QCD Perturbation Theory (Lattice?)

• ... but the scale dependence of PDFs is governed by the DGLAP evolution equations

∂qi (x,µ2 )

∂lnµ2 = α s (µ2 )

2πPqq (x)⊗ qi (x,µ

2 )⎡⎣ ⎤⎦ +α s (µ

2 )2π

Pqg ⊗ g(x,µ2 )⎡⎣ ⎤⎦

∂g(x,µ2 )∂lnµ2 = α s (µ

2 )2π

Pgq (x)⊗ qi (x,µ2 )+ qi (x,µ

2 )( )i∑⎡

⎣⎢⎤⎦⎥+ α s (µ

2 )2π

Pgg ⊗ g(x,µ2 )⎡⎣ ⎤⎦

• ... where the splitting functions (Pij) can be computed in Perturbation Theory and are known up to NNLO

[LO - Dokshitzer; Gribov, Lipatov; Altarelli, Parisi (1977)][NLO - Floratos, Ross, Sachrajda; Gonzalez-Arroyo, Lopez, Yndurain; Curci, Furmanski, Petronzio (1981)]

[NNLO - Moch, Vermaseren, Vogt (2004)]

The PDF fitting gameThe players

Collaboration Authors arXiv

ABM

CTEQ/TEA

GJR

HERAPDF

MSTW

NNPDF

S. Alekhin, J. Blümlein, S. Moch1105.5349, 1101.5261, 1107.3657,

0908.3128, 0908.2766, …

M. Guzzi J. Huston, H.-L. Lai, P. Nadolsky, J. Pumplin, D. Stump, C.-P. Yuan

1108.5112, 1101.0561, 1007.2241, 1004.4624, 0910.4183, 0904.2424, 0802.0007, …

M. Glück, P. Jimenez-Delgado, E. Reya 1003.3168, 0909.1711, 0810.4274, …

H1 and ZEUS Collaborations 1107.4193, 1006.4471, 0906.1108, …

A. Martin, J. Stirling, R. Thorne, G. Watt 1107.2624, 1006.2753, 0905.3531, 0901.0002, …

R. D. Ball, V. Bertone, S. Carrazza, F. Cerutti, C. S. Deans, L. Del Debbio, S. Forte, AG,

N. P. Hartland, J. I. Latorre, J. Rojo, M. Ubiali

1110.2483, 1108.2758, 1107.2652, 1103.2369, 1102.3182, 1101.1300, 1005.0397, 1002.4407,

0912.2276, 0906.1958, …

The PDF fitting gameStatus of PDF fits

DATASET PERT. ORDER

HQ TREATMENT αs PARAM. UNCERT.

ABM11

CT10

JR09

HERAPDF1.5

MSTW08

NNPDF2.3

DISDrell-Yan NLO

NNLOFFN

(BMSN)Fit

(multiple values available)

6 indep. PDFsPolynomial(25 param.)

Hessian(Δ𝛘2=1)

GlobalLO

NLONNLO

GM-VFNS(S-ACOT)

External(multiple values

available)


Hessian(Δ𝛘2=100)

DISDrell-Yan

JetsNLO

NNLOFFNVFN Fit


Hessian(Δ𝛘2=1)

DIS (HERA) NLONNLO

GM-VFNS(TR)


available)


Hessian(Δ𝛘2=1)

GlobalLO

NLONNLO

GM-VFNS(TR)

Fit(multiple values

available)


Hessian(Δ𝛘2≃25)

GlobalLO

NLONNLO

GM-VFNS(FONLL)


available)

7 indep. PDFsNeural Nets(259 param.)

Monte Carlo

The PDF fitting gameStatus of PDF fits - parton luminosities

Good compatibility among global fitsDifferences are more marked when comparing to restricted dataset fits

Parton Luminosities at 8 TeV

15 Juan Rojo QCD@LHC Workshop, 20/08/2012

XM210 310

Glu

on -

Glu

on L

umin

osity

0.80.850.9

0.951

1.051.1

1.151.2

1.251.3

= 0.119S_LHC 8 TeV - Ratio to NNPDF2.3 NNLO,

NNPDF2.3 NNLO

CT10 NNLO

MSTW2008 NNLO


XM210 310

Glu

on -

Glu

on L

umin

osity

0.80.850.9

0.951

1.051.1

1.151.2

1.251.3


NNPDF2.3 NNLO

ABM11 NNLO

HERAPDF1.5 NNLO


Gluon-Gluon

Reasonable agreement for between global PDF sets (but larger differences near M=125 GeV) Larger differences with non-global sets: ABM11 softer gg luminosity, specially at large

masses, HERAPDF1.5 similar central values but larger PDF uncertainties

The PDF fitting gameStatus of PDF fits - LHC cross-section

LHC cross sections at 8 TeV


) [pb

]+

(Wm

6.6

6.8

7

7.2

7.4

7.6

7.8 = 0.119S_) - VRAP NNLO - +(WmLHC 8 TeV

NNPDF2.3MSTW08CT10ABM11HERAPDF1.5CMS 2012

) [pb

]0

(Zm

1.061.08

1.11.121.141.161.181.2

1.221.241.26

= 0.119S_) - VRAP NNLO - 0(ZmLHC 8 TeV NNPDF2.3MSTW08CT10ABM11HERAPDF1.5CMS 2012

CMS

CMS

Good agreement between all PDFs but ABM11, which leads to larger cross sections

CMS data disfavor ABM11, but experimental errors still large

W+

Z0

More stringent constraints from 8 TeV differential distributions


) [pb

]+

(Wm

6.6

6.8

7

7.2

7.4

7.6



) [pb

]0

(Zm

1.061.08

1.11.121.141.161.181.2

1.221.241.26


CMS

CMS



W+

Z0




(tt) [

pb]

m

200

210

220

230

240

250

260

270 = 0.119S_+NNLL - approx(tt) - Top++ v1.2 NNLOmLHC 8 TeV


CMS

(tt) [

pb]

m

180

200

220

240

260

280+NNLL - ABM11approx(tt) - Top++ v1.2 NNLOmLHC 8 TeV

= 0.114s_ = 0.117s_ = 0.118s_ = 0.119s_

CMS data

CMS

Good agreement between all PDFs (but ABM systematically lower, even for !S = 0.119 )

CMS data clearly disfavor small values of !S . Use these data to extract !S from the total cross section?

More stringent constraints on PDFs from differential distributions from ATLAS and CMS

Also cross section ratios between 8 TeV and 7 TeV very useful for PDF information (arXiv:1206.3557)

tT

tT

LHC data are already starting to discriminate among predictions

The PDF fitting gameStatus of PDF fits - LHC cross-section



) [pb

]+

(Wm

6.6

6.8

7

7.2

7.4

7.6



) [pb

]0

(Zm

1.061.08

1.11.121.141.161.181.2

1.221.241.26


CMS

CMS



W+

Z0



) [pb

]+

(Wm

6.6

6.8

7

7.2

7.4

7.6



) [pb

]0

(Zm

1.061.08

1.11.121.141.161.181.2

1.221.241.26


CMS

CMS



W+

Z0




(tt) [

pb]

m

200

210

220

230

240

250

260

270 = 0.119S_+NNLL - approx(tt) - Top++ v1.2 NNLOmLHC 8 TeV


CMS

(tt) [

pb]

m

180

200

220

240

260

280+NNLL - ABM11approx(tt) - Top++ v1.2 NNLOmLHC 8 TeV

= 0.114s_ = 0.117s_ = 0.118s_ = 0.119s_

CMS data

CMS

Good agreement between all PDFs (but ABM systematically lower, even for !S = 0.119 )

CMS data clearly disfavor small values of !S . Use these data to extract !S from the total cross section?

More stringent constraints on PDFs from differential distributions from ATLAS and CMS

Also cross section ratios between 8 TeV and 7 TeV very useful for PDF information (arXiv:1206.3557)

tT

tT


(H) [

pb]

m

19.5

20

20.5

21

21.5

22

22.5 = 0.119S_LHC 8 TeV - iHixs 1.3 NNLO -

NNPDF2.3MSTW08CT10ABM11HERAPDF1.5

For a Higgs boson of 125 GeV, and a common !S = 0.119 value, all PDF sets within the PDF4LHC envelope

ABM11 in agreement with global PDF sets for same !S value

HERAPDF1.5 errors larger due to gluon parametrization uncertainties in a DIS only fit. To be improved with HERA jets.

Available PDF4LHC recommendation did clearly a good job: updated PDF sets if anything have confirmed its validity

These results will be useful to discuss future PDF4LHC recommendations

gg->H

LHC data are already starting to discriminate among predictions

NNPDF MethodologyMain ingredients

Monte Carlo determination of uncertaintiesNo need to rely on linear propagation of errorsPossibility to test the impact of non-gaussianly distributed uncertaintiesPossibility to test for non-gaussian behaviour of uncertainties of fitted PDFs

Parametrization of PDFs using Neural NetworksProvide an unbiased parametrization

Determine the best fit PDFs using Cross-ValidationEnsures proper fitting, avoiding overlearning

NNPDF Methodology... in a Nutshell

Generate Nrep Monte Carlo replicas of the experimental data, taking into account all experimental correlations

Fit a set of Parton Distribution Functions, parametrized at the initial scale using Neural Networks, to each replica

Expectation values for observables are then given by

.... and corresponding formulae are used to compute uncertainties, correlations, etc.

Reweighting (NN)PDFsAssessing the impact of new data on PDF fits

[R. D. Ball et al., arXiv:1012.0836]


The Nrep replicas of a NNPDF fit give the probability density in thespace of PDFs

Expectation values for observables computed as

hF [fi(x , Q2)]i =1

Nrep

NrepX

k=1

F⇣

f (net)(k)i (x , Q2)

⌘

(... the same is true for errors, correlations, etc.)

We can assess the impact of including new data in the fit updatingthe probability density distribution without refitting.

A. Guffanti (NBIA & Discovery Center) PDFs@LHC 54 / 69

NNPDF MethodologyMonte Carlo replicas generation

Monte Carlo replicas are generated according to the distribution

where ri are (gaussianly distributed) random numbers

Validate Monte Carlo replicas against experimental data

O(1000) replicas needed to reproduce correlations in experimental data to percent accuracy


Generate artificial data according to distribution

O(art) (k)i = (1 + r (k)

N �N)

"O(exp)

i +

NsysX

p=1

r (k)p �i,p + r (k)

i,s �

is

#

where ri are univariate (gaussianly distributed) random numbers

Validate Monte Carlo replicas against experimental data(statistical estimators, faithful representation of errors, convergence rateincreasing Nrep)

O(1000) replicas needed to reproduce correlations to percent accuracy

A. Guffanti (NBIA & Discovery Center) NNPDF4LHC 13 / 40


Generate artificial data according to distribution

O(art) (k)i = (1 + r (k)

N �N)

"O(exp)

i +

NsysX

p=1

r (k)p �i,p + r (k)

i,s �

is

#

where ri are univariate (gaussianly distributed) random numbers

Validate Monte Carlo replicas against experimental data(statistical estimators, faithful representation of errors, convergence rateincreasing Nrep)

O(1000) replicas needed to reproduce correlations to percent accuracy

A. Guffanti (NBIA & Discovery Center) NNPDF4LHC 13 / 40

Reweighting (NN)PDFsThe reweighting idea

The N replicas of an NNPDF fit give the probability density in the space of PDFs

Expectation values for observables are computed as

We can then assess the impact of including new data in the fit updating the probability density without performing a complete refit




The Nrep replicas of a NNPDF fit give the probability density in thespace of PDFs

Expectation values for observables computed as

hF [fi(x , Q2)]i =1

Nrep

NrepX

k=1

F⇣


⌘

(... the same is true for errors, correlations, etc.)

We can assess the impact of including new data in the fit updatingthe probability density distribution without refitting.


[R. D. Ball et al, arXiv:1012.0836][R. D. Ball et al, arXiv:1108.1758]

We can apply Bayes Theorem to determine the conditional probability of the PDF upon inclusion of the new data

Averages over the sample are no weighted sums

and the weights are given by

Reweighting (NN)PDFsThe reweighting formula

[R. D. Ball et al, arXiv:1012.0836][R. D. Ball et al, arXiv:1108.1758]




According to Bayes Theorem we have

Pnew({f}) = N�P(�2|{f})Pinit({f}) , P(�2|{f}) = [�2(y , {f})]ndat�1

2 e��2(y,{f})2

Averages over the sample are now weighted sums

hF [fi(x , Q2)]i =NrepX

k=1

wkF⇣


⌘

where the weights are

wk =[�2(y , fk )]

ndat�12 e��2(y,fk )

2

PNrepi=1 [�2(y , fi)]

ndat�12 e��2(y,fi )

2







2 e��2(y,{f})2



k=1

wkF⇣


⌘


wk =[�2(y , fk )]


2



2







2 e��2(y,{f})2



k=1

wkF⇣


⌘


wk =[�2(y , fk )]


2



2


Reweighting (NN)PDFsValidating the reweighting procedure

We started from a fit to DIS and Drell-Yan data and included Tevatron inclusive jet data (CDF & D0) via refitting and reweighting

Reweighting with the two dataset at the same time or separately (in either order) yields identical results

Reweighting and refitting yield statistically equivalent results in the region constrained by the new data

CDF D0 CDF+D0Data points 76 110 186

Neff 290.8 565.8 334.5

Table 1: Datasets used in the Tevatron Run II inclusive jet reweighting exercise. For eachset the number of data points and the effective number of replicas of the reweighted setof Nrep = 1000 replicas are given.

x0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7

] 02xg

[x,Q

0

0.2

0.4

0.6

0.8

1Prior (NNPDF 2.0 DIS+DY)Refitted (NNPDF 2.0)W(CDF+D0)W(CDF)W(D0)W(D0)W(CDF)

)20

CDF/D0 Inclusive Jets - xg(x,Q

x0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7

] 02xg

[x,Q

-1

-0.5

0

0.5

1

1.5

2

2.5

3Prior (NNPDF 2.0 DIS+DY)Refitted (NNPDF 2.0)W(CDF+D0)W(CDF)W(D0)W(D0)W(CDF)

), ratio to NNPDF2.0 DIS+DY20

CDF/D0 Inclusive Jets - xg(x,Q

Figure 6: Comparison of the large-x gluon PDF for prior set, reweighted sets with differentsuccessive reweighting orders and refitted set, when the jet data of Table 1 are included inthe NNPDF2.0 NLO DIS+DY fit. Results are shown at Q2 = 2 GeV2, both in absolutescale (left) and as a ratio to the prior (right).

4.2 Tevatron Inclusive Jets

The first exercise we present is an extension of the reweighting proof-of-concept in Section 4of [14]. There, Run II Tevatron inclusive jet data production were included by reweightinga PDF set extracted from a NLO fit to DIS and Drell-Yan data (NNPDF2.0 DIS+DY) andthe results compared to those obtained from a fit which included the same DIS, Drell-Yanand inclusive jet datasets all treated in the same way (NNPDF2.0).

In this Section we look again at the inclusion via reweighting of the same datasets,namely the CDF Run II-kt and D0 Run II-cone inclusive jet data in the NNPDF2.0DIS+DY fit, but we now focus on comparing the results obtained in the following twocases:

(a) the two new datasets are included by reweighting the prior fit in a single step withboth datasets;

(b) one of the datasets is included by reweighting, an unweighted set of PDFs is con-structed using the procedure detailed in Section 3, and finally the latter set isreweighted again with the second dataset.

We will carry out the successive reweighting procedure (b) twice, exchanging the orderin which the CDF and D0 datasets are included, in order to test the commutativity ofthe procedure. A final unweighting is performed for all the reweighted sets and the PDFcomparisons and computations of distances are performed using these unweighted sets.

The number of data points and the effective number of replicas Neff after reweightingwith these data of a set of Nrep = 1000 replicas are summarized in Table 1. In each

16

NNPDF2.3 Dataset

x-510 -410 -310 -210 -110 1

]2 [

GeV

2 T /

p2

/ M

2Q

1

10

210

310

410

510

610

710NMC-pdNMCSLACBCDMS

HERAI-AVCHORUSFLH108

NTVDMNZEUS-H2ZEUSF2CH1F2CDYE605DYE886CDFWASYCDFZRAPD0ZRAP

CDFR2KTD0R2CONATLAS-WZ-36pbCMS-WEASY-840PBLHCB-WZ-36pbATLAS-JETS-10

NNPDF2.3 dataset Experiment DataFixed Target DIS 1952

HERA DIS 834Fixed Target DY 318

Tevatron W/Z 70Tevatron Jets 186

LHC W/Z 56LHC Jets 90

3506 data points(in the NNLO global fit)

NNPDF2.3The LHC data - Fit quality

NLO NNPDF2.3 noLHC NNPDF2.3NMCpd

NMCSLAC

BCDMSHERA-I

CHORUSNuTeV

DYE605DYE866

CDFWASYCDFZRAPD0ZRAP

ATLAS-WZCMS-WEASY

LHCb-WZCDFR2KTD0R2CON

ATLAS-JETS-2010

0.93 0.931.59 1.611.28 1.261.20 1.191.01 1.001.09 1.100.42 0.450.85 0.881.24 1.281.45 1.541.77 1.790.57 0.571.37 1.271.50 1.041.24 1.210.60 0.610.84 0.841.57 1.55

Compare the quality of the fit to LHC data before and after inclusion in the global fit

Including LHC data in the fit improves the quality of their description, w/o deteriorating quality of the fit to other datasets

Moderate impact of the LHC data, supporting consistency of the global fit framework

Fit quality is comparable at NLO and NNLO, thought the former marginally better

NNPDF2.3The LHC data - Fit quality

NNLO NNPDF2.3 noLHC NNPDF2.3NMCpd

NMCSLAC

BCDMSHERA-I

CHORUSNuTeV

DYE605DYE866

CDFWASYCDFZRAPD0ZRAP

ATLAS-WZCMS-WEASY

LHCb-WZCDFR2KTD0R2CON

ATLAS-JETS-2010

0.94 0.941.56 1.571.04 1.021.28 1.291.03 1.011.07 1.060.48 0.551.07 1.021.61 1.621.66 1.702.15 2.120.64 0.631.94 1.461.37 0.961.33 1.220.67 0.670.94 0.931.45 1.42

Compare the quality of the fit to LHC data before and after inclusion in the global fit

Including LHC data in the fit improves the quality of their description, w/o deteriorating quality of the fit to other datasets

Moderate impact of the LHC data, supporting consistency of the global fit framework

Fit quality is comparable at NLO and NNLO, thought the former marginally better

NNPDF2.3Collider fit - are we there yet?

It is the fit we would love to haveOnly high energy data: minimize the effects of higher-twist contributionsOnly proton data: no assumptions based on models for nuclear corrections

x0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7)

02 (x, Q3xT

NNPDF2.3 NLO

NNPDF2.3 NLO Collider-Only

) 02 (x, Q3xT

x-410 -310 -210 -110

0

1

2

3

4

5 )

02xg ( x, Q

NNPDF2.3 NLO

NNPDF2.3 NLO Collider-Only

)02xg ( x, Q

Gluon distribution is very well constrained both at small-x (HERA) and large-x (Tevatron/LHC jets)

PDF combinations sensitive to light flavour separationhave substantially larger uncertainties (missing constraints from fixed target DIS/DY data)

Uncertainties on “fixed target” observables are still unacceptably large

... things can only get better with more LHC data coming (W+c, low mass DY, photons, high pt Z/W ...)

NNPDF2.3Phenomenology - parton luminosities

Reduction in uncertainty on gluon-gluon luminosity for larger final state invariant masses when going from NNPDF2.1 to NNPDF2.3

NNPDF2.3 quark-antiquark luminosity at large invariant masses somewhat smaller than NNPDF2.1

NNPDF2.3Phenomenology - W/Z production

Mostly sensitive to quark parton luminositiesPredictions from NNPDF2.3 sets are compatible with each other and with predictions obtained using the NNPDF2.1 global setLargest differences with collider only fit, although the latter has larger uncertainties

[nb]

il)B+

(Wm

5.5

6

6.5

7

7.5NNPDF + VRAP

NLO, 7 TeVNLO, 8 TeVNNLO, 7 TeVNNLO, 8 TeV

2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

il)B-

(Wm

3.8

4

4.2

4.4

4.6

4.8

5

5.2NNPDF + VRAP


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

ll)B0

(Zm

0.8

0.9

1

1.1

1.2

NNPDF + VRAPNLO, 7 TeVNLO, 8 TeVNNLO, 7 TeVNNLO, 8 TeV

2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

(H) [

pb]

m

8

10

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14

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18

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NNPDF + iHixs 1.3NLO, 7 TeVNLO, 8 TeVNNLO, 7 TeVNNLO, 8 TeV

2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

(tt) [

pb]

m

140

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NNPDF + Top++NLO, 7 TeVNLO, 8 TeVNNLO, 7 TeVNNLO, 8 TeV

2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

[nb]

il)B+

(Wm

5.5

6

6.5

7

7.5NNPDF + VRAP


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

il)B-

(Wm

3.8

4

4.2

4.4

4.6

4.8

5

5.2NNPDF + VRAP


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

ll)B0

(Zm

0.8

0.9

1

1.1

1.2


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

(H) [

pb]

m

8

10

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2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

(tt) [

pb]

m

140

160

180

200

220

240


2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

NNPDF2.3Phenomenology - top/Higgs production

[nb]

il)B+

(Wm

5.5

6

6.5

7

7.5NNPDF + VRAP


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

il)B-

(Wm

3.8

4

4.2

4.4

4.6

4.8

5

5.2NNPDF + VRAP


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll

[nb]

ll)B0

(Zm

0.8

0.9

1

1.1

1.2


2.1 2.32.3noLHC

2.3coll

2.1 2.32.3noLHC

2.3coll(H

) [pb

]m

8

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2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

(tt) [

pb]

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2.1 2.3 2.3noLHC2.3coll

2.1 2.3 2.3noLHC2.3coll

Mostly sensitive to quark parton luminositiesPredictions from NNPDF2.3 sets are compatible with each other and with predictions obtained using the NNPDF2.1 global setLargest differences with collider only fit, although the latter has larger uncertainties

The LHCLepton-hadron collision at the LHC

The LHeC is a proposed facility at CERN, where a (7 TeV) LHC proton beam is brought into collision with a lepton beam

The LHeC is designed to operate simultaneously with the LHC

Two options for the lepton beam are considered

Linac-Ring optionRing-Ring option

Many more details can be found in the recently published Conceptual Design Report (arXiv:1206.2913)

ICHEP&LHeC&Max&Klein&7.7.2012& 1&

The&LHeC&Project&&&

Max&Klein&University&of&Liverpool&

&For&the&LHeC&Study&Group&

&Brief&overview&on&the&&Status&and&Prospects&

&&&&

at&this&conference:&A.Polini&–&Detector&C.Glasman&–&QCD&P.Newman&–&eA&U.Klein&–&Higgs&

&ICHEP&2012&Melbourne&7.7.&

The LHCPotential to constrain PDFs

The measurements at the LHeC will nicely complement the pp and pA measurements from the LHC experiments

Unique possibility to explore the small-x region in DIS

Precise measurements of the Neutral and Charrged Current DIS cross-section at large-x for accurate flavour separation

Precise determination of heavy flavour parton distributions

ComplemenNng$the$LHC$with$ep/A$LHC$partons:$W,Z$+c,b$new$constraints$but$severely$limited$in$x,Q2$range$

Discoveries$at$the$LHC$will$be$at$high$masses:$large$x$and$very$high$Q2$which$require$high$s,$lumi$of$LHeC$for$precision$PDFs$(u,d,xg$mainly)$

If$the$Higgs$exists,$its$study$will$become$a$major$field$of$research:$ep:$$WW$H$$bbar$(CP$odd/even?)$

top$distribuNon$in$the$proton$TDF$

IF$RP$is$violated$and$LQ$or$RPV$SUSY$discovered:$LHeC$is$uniquely$suited$

AA:$QGP:$study$iniNal$state$in$eA$Resolve$parton$distribuNons$in$nuclei$

LHeC$is$unique$in$various$areas,$e.g.:$$Low$x$and$saturaNon$physics$Strong$coupling$constant$to$0.1%$level$

In$Drell]Yan$kinemaNcs:$mass$and$rapidity$relate$to$Q2$and$x$

DIS$

$

The LHCPotential to constrain PDFs

The measurements at the LHeC will nicely complement the pp and pA measurements from the LHC experiments

Unique possibility to explore the small-x region in DIS

Precise measurements of the Neutral and Charrged Current DIS cross-section at large-x for accurate flavour separation

Precise determination of heavy flavour parton distributions

Partons, QCD and Low x Physics at the LHeC 10

LHeC physics: proton PDFs• PDFs are extracted from fits to data assuming a• functional form for x at a given Q2

min value and then• evolved via DGLAP evolution equations• Current PDF status from HERA:

0

0.2

0.4

0.6

0.8

1

-410 -310 -210 -110 10

0.2

0.4

0.6

0.8

1

HERAPDF1.5 NNLO (prel.)

exp. uncert.

model uncert. parametrization uncert.

x

xf 2 = 2 GeV2Q

vxu

vxd

0.05)×xS ( 0.05)×xg (

HER

APD

F St

ruct

ure F

unct

ion

Wor

king

Gro

upM

arch

201

1

H1 and ZEUS HERA I+II PDF Fit

0

0.2

0.4

0.6

0.8

1 • Impact of LHeC• data expected to• be large thanks to− new kinematic− range− huge luminosity− polarised beams− deuteron beams− high precision data

⇒ LHeC has the potential to provide significant⇒ LHeC has the potential to provide significant⇒ constraints to the PDFs →

u valence

d valence

gluon

Q2=1.9 GeV2

high x

• The reduction of uncertainties in nuclear PDFs are• presented in the talk on heavy ions

LHeC constraint

Claudia Glasman (Universidad Autonoma de Madrid) ICHEP2012 (Melbourne), 4-11 July 2012

Partons, QCD and Low x Physics at the LHeC 10

LHeC physics: proton PDFs• PDFs are extracted from fits to data assuming a• functional form for x at a given Q2

min value and then• evolved via DGLAP evolution equations• Current PDF status from HERA:

0

0.2

0.4

0.6

0.8

1

-410 -310 -210 -110 10

0.2

0.4

0.6

0.8

1

HERAPDF1.5 NNLO (prel.)

exp. uncert.

model uncert. parametrization uncert.

x

xf 2 = 2 GeV2Q

vxu

vxd

0.05)×xS ( 0.05)×xg (

HER

APD

F St

ruct

ure

Func

tion

Wor

king

Gro

upM

arch

201

1

H1 and ZEUS HERA I+II PDF Fit

0

0.2

0.4

0.6

0.8

1 • Impact of LHeC• data expected to• be large thanks to− new kinematic− range− huge luminosity− polarised beams− deuteron beams− high precision data

⇒ LHeC has the potential to provide significant⇒ LHeC has the potential to provide significant⇒ constraints to the PDFs →⇒ A precise value of αs from DIS will also be possible⇒ at LHeC

u valence

d valence

gluon

Q2=1.9 GeV2

low x

LHeC constraint

Claudia Glasman (Universidad Autonoma de Madrid) ICHEP2012 (Melbourne), 4-11 July 2012

Conclusions & Outlook

Parton Distribution Function are an essential ingredient of theoretical predictions for observables at hadron-hadron and lepton-hadron colliders

The NNPDF methodology is ideally suited to tackle the shortcomings of the standard PDF fitting methodology

The reweighting technique can be used to quickly and reliably assess the impact of new data in parton distribution functions fits without the need for refitting

NNPDF2.3 is the first global PDF fit to include LHC data

The proposed LHeC experiment will be a unique tool to understand the structure on the proton and accurately determine parton densities

(NN)PDFs, the LHC & the LHeC - Indiana University · Monte Carlo. The PDF ﬁtting game Status of...

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