High-Q2 physics at the LHC · High-Q2 physics at the LHC Frank Krauss Institute for Theoretical Physics TU Dresden DIS 2006, KEK, 23.4.2006 F. Krauss TUD ... H1 150 < Q2 < 200 GeV2

Introduction Fitting PDFs Defining Jets Multijets Underlying event Summary

High-Q2 physics at the LHC

Frank Krauss

Institute for Theoretical PhysicsTU Dresden

DIS 2006, KEK, 23.4.2006

F. Krauss TUD



Outline

1 Introduction

2 Knowledge of the initial state: PDFs

3 Knowledge of QCD evolution: Jets

4 Knowledge of background topologies: Multijets

5 Knowledge of non-factorizable QCD: Underlying event

6 Summary

F. Krauss TUD



Reminder: Physics @ LHCMany interesting signals:Higgs (or alternative EWSB), SUSY, ED’s, . . .

But: Severe backgrounds in nearly all channels,(almost always with large influence of QCD)

=⇒ depend on detailed understanding of QCD.

Examples:

Central jet-veto in VBF (Higgs)

Multi-jet backgrounds for SUSY (e.g. Z+jets)

Todays signals = tomorrows backgrounds.

F. Krauss TUD



Scope of this talkYES: maybe lots of interesting physics at LHC

BUT: (nearly) nothing comes for free:

A signal is what remains after background subtraction.How well do we understand bread-and-butter physics?

SO: I won’t talk too much about Higgs, BSM, etc..

I’ll talk about our understanding of QCDas far as I can tell.

F. Krauss TUD



Fitting PDFs

MotivationPDFs are basic input for cross section calculations

PDFs so far mainly determined from DIS data

Uncertainties remain, typically around 5%-10%,larger for instance in high-Q2 gluons

Prepare for inclusion of LHC data in fits

Ongoing projects: FastNLO & NLO@Gridsee talks by M.Wobisch and D.Clement

F. Krauss TUD



Fitting PDFs

Input of hadron collidersPDFs usually measured in DIS,Input of hadron colliders: gluon @ high Q2.

Sensitivity through

σpp→X (s) =

∫dx1dx2fi (x1, Q2

F )fj(x2, Q2F )σij→X (s, µ2

R) ,

where fi,j = PDFs, s ∼ x1x2s.

Idea for extraction of PDF: Compare σ @ NLO with data.

Problem: Duration of NLO calculation.

F. Krauss TUD



Fitting PDFs

Quality of theory vs. data

1

10

10 2

10 102

103

inclusive jet productionin hadron-induced processes

fastNLOwww.cedar.ac.uk/fastnlo

DIS

pp-bar

√s = 300 GeV

√s = 630 GeV

√s = 1800 GeV

√s = 1960 GeV

100 < Q2 < 500 GeV2

500 < Q2 < 10 000 GeV2

H1 150 < Q2 < 200 GeV2

H1 200 < Q2 < 300 GeV2

H1 300 < Q2 < 600 GeV2

ZEUS 125 < Q2 < 250 GeV2

ZEUS 250 < Q2 < 500 GeV2

H1 600 < Q2 < 3000 GeV2

ZEUS 500 < Q2 < 1000 GeV2

ZEUS 1000 < Q2 < 2000 GeV2

ZEUS 2000 < Q2 < 5000 GeV2

DØ |y| < 0.5

CDF 0.1 < |y| < 0.7DØ 0.0 < |y| < 0.5DØ 0.5 < |y| < 1.0

CDF cone algorithmCDF kT algorithm

(× 100)

(× 40)

(× 8)

(× 3)

(× 1)

all pQCD calculations by fastNLO:αs(MZ)=0.118 | CTEQ6.1 PDFs | µr = µf = pT

DIS in NLO | pp in NLO + NNLO-NLL | plus non-perturbative corrections

pT (GeV/c)

data

/ th

eory

Data from CDF, PRD64 (2001) 012001

F. Krauss TUD



Fitting PDFs

Idea for accelerationhep-ph/0510324 by T.Carli, G.Salam, F.Siegert

Replace integration by summation (MC integration):

σ =∑

x1, x2, Q2

Wσ · WPDF

Bin σ in 2 dims =⇒ Wσ

Bin fi (x1, Q2F )fj(x2, Q2

F ) in 3 dims =⇒ WPDF

Interpolate in between

Pre-calculate Wσ and use for fast evaluation/fitting of PDF

F. Krauss TUD



Fitting PDFs

FastNLOFlattening the PDFs

10-7

10-6

10-5

10-4

10-3

10-2

10-1

11010 2

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

x ⊗

PD

F(x)

10-7

10-6

10-5

10-4

10-3

10-2

10-1

11010 2

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

10-7

10-6

10-5

10-4

10-3

10-2

10-1

11010 2

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

10-3

10-2

10-1

1

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

w(x

) ⊗ P

DF(

x)

10-3

10-2

10-1

1

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

10-3

10-2

10-1

1

-2 -1 0

10-410-3 10-2 10-1 0.5 0.9

xparton

CTEQ6.1M

µf = 5 GeVµf = 50 GeVµf = 500 GeVµf = 5000 GeV

original (unweighted) PDFs

reweighted PDFs: w(x) = x−3/2 (1 − 0.99 x)3

gluon sum of quarks sum of anti-quarks

Accuracy of Grid interpolation

0.996

0.998

1

1.002

1.004

200 400

0.0 < η < 0.5

fast

NLO

app

roxi

mat

ion

/ ref

eren

ce

0.996

0.998

1

1.002

1.004

200 400

1.0 < η < 1.5

0.996

0.998

1

1.002

1.004

200 400

0.5 < η < 1.00.996

0.998

1

1.002

1.004

200 400

1.5 < η < 2.00.996

0.998

1

1.002

1.004

100 150 200

2.0 < η < 3.0

ET (GeV)

No. bins to cover the x-range: 8 x-bins10 x-bins12 x-bins16 x-bins

pp-bar: incl. jets at sqrt(s)=1.8 TeVDØ Collaboration hep-ex/0011036

NLO@GridAccuracy of Grid interpolation

[GeV]TE500 1000 1500 2000 2500 3000 3500 4000 4500 5000

[GeV]TE500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0.9999

0.99995

1

1.00005

1.0001

1.00015

1.0002

Impact on gluon PDF

F. Krauss TUD



Defining jets

Motivation(Nearly) all physics signals are with jets:

VBF: Two forward “tag”-jetsgluinos: 4 jets + ET/

Large systematic uncertainties (steeply falling spectra)

Need to define jet with good properties(better than “bunch of hadronic energy”)

F. Krauss TUD



Defining jets

Theory vs. data from TevatronData from D0, PRL 94 (2005) 221801, plot by M.Zielinski

∆φ dijet (rad)

1/σ di

jet

dσdi

jet /

d∆φ

dije

t

pT max > 180 GeV (×8000)130 < pT

max < 180 GeV (×400)100 < pT

max < 130 GeV (×20) 75 < pT

max < 100 GeV

NLOSHERPAHERWIG

D0

10-3

10-2

10-1

1

10

10 2

10 3

10 4

10 5

0.5 0.6667 0.8333 1

π/2 2π/3 5π/6 π

F. Krauss TUD



Defining jets

Algorithms

Cone algorithms (iterative, midpoint)Basic idea/algorithm

Find high ET binsCone in φ-η around them with radius R

Cluster to jet (add momenta)Differences in treatment of overlapping cones

k⊥-algorithmsBasic idea/algorithm

Define “k⊥-metric” (with “radius” R)Cluster until k

ij⊥≤ kcrit

⊥

F. Krauss TUD



Defining jets

Work by D.Benedetti et al., in hep-ph/0604120

CriteriaAngular distance αi

jp:∆R(pi

, j i ) of i th parton p to its jet j

Energy difference β ijp:

Distance in σ from fitted curve

(GeV)jetTE

0 20 40 60 80 100 120 140 160 180 200

part

on/E

jet

E

0

0.2

0.4

0.6

0.8

1

1.2

maxα0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

max

jpβ

0

1

2

3

4

5

6well clustered events

badly clustered events

F. Krauss TUD



Defining jets: Testing the iterative cone-algorithm

Selected/well-clustered & selected events in t tH

jet cone radius0.2 0.3 0.4 0.5 0.6 0.7 0.8

Sel

ectio

n E

ffic

ienc

y (%

)

10

20

30

40

50

60

70

80

90

100

2 jets4 jets6 jets8 jets

jet cone radius0.2 0.3 0.4 0.5 0.6 0.7 0.8

Frac

Goo

d (%

)1


F. Krauss TUD



Defining jets: Testing the k⊥-algorithm

Selected/well-clustered & selected events in t tH

R-parameter0.2 0.4 0.6 0.8 1

Sel

ectio

n E

ffic

ienc

y (%

)

10

20

30

40

50

60

70

80

90

100


R-parameter0.2 0.4 0.6 0.8 1

Frac

Goo

d (%

)1

10 2 jets4 jets6 jets8 jets

F. Krauss TUD



Multijets: Motivation

Higgs searches Central jet veto in VBF

Signal/background ratiodepends on central jet veto.(rapidity gap between two “tagging jets”,

=⇒ beautiful signal at leading order)

But: How many jets come athigher orders?=⇒ currently studied.

F. Krauss TUD



Multijets: Motivation

SUSY searches

Large σprod

Many hard jets.

Meff =∑

phard⊥

.

Quick Discovery?

F. Krauss TUD



Multijets: MC-Comparison

Introducing SHERPAT.Gleisberg, S.Hoche, F.K., A.Schalicke, S.Schumann and J.C.Winter, JHEP 0402 (2004) 056

New event generator in C++;

Matrix elements @ LO,combined with parton shower

(S.Catani et al. JHEP 0111 (2001) 063

F.K., JHEP 0208 (2002) 015);

Hadronization by Pythia;

Underlying event a la Pythia(old version), showers added.

resummed in PS

exact MELO 5jet, but alsoNLO 4jet

L

αn

mNLLexact ME

LO 4jet

4

4

4

4

4

5

5

5

5

5

5

5

F. Krauss TUD




p⊥ of Z -bosons in pp → Z + X @ TevatronData from CDF, Phys. Rev. Lett. 84 (2000) 845

/ GeV Z P0 20 40 60 80 100 120 140 160 180 200

10-3

10-2

10-1

1

10pt Z

Z + 0 jet

Z + 1 jetZ + 2 jetCDF

GeVpb

/

dPσd

/ GeV Z P0 5 10 15 20 25 30 35 40 45 50

G

eVpb /

dP

σd

1

10

pt Z

Z + 0 jet

Z + 1 jetZ + 2 jetCDF

F. Krauss TUD




Jet rates in pp → Z + X @ Tevatron(D0-Note 5066)

0 1 2 3 4 5 6

1

10

210

310

410

0 1 2 3 4 5 6

1

10

210

310

410

data w/stat errordata w/stat & sys errorPythia range statPythia range stat & sys

D0 RunII Preliminary

Jet Multiplicity

Nr.

of E

vent

s

0 1 2 3 4 5 60.2

1

234

Jet Multiplicity

Dat

a / P

YTH

IA

0 1 2 3 4 5 6

1

10

210

310

410

0 1 2 3 4 5 6

1

10

210

310

410

data w/stat errordata w/stat & sys errorSherpa range statSherpa range stat & sys


Jet Multiplicity

Nr.

of E

vent

s

0 1 2 3 4 5 60.2

1

234

Jet Multiplicity

Dat

a / S

HE

RP

A

F. Krauss TUD




Jet spectra (1st jet) in pp → Z + X @ Tevatron(D0-Note 5066)

50 100 150 200 250 300 350

1

10

210

310

50 100 150 200 250 300 350

1

10

210

310



jet [GeV]st 1T

p

Nr.

of E

vent

s

50 100 150 200 250 300 3500.2

1

234

jet [GeV]st 1T

p

Dat

a / P

YTH

IA

50 100 150 200 250 300 350

1

10

210

310

50 100 150 200 250 300 350

1

10

210

310



jet [GeV]st 1p

Nr.

of E

vent

s

50 100 150 200 250 300 3500.2

1

234

jet [GeV]st 1T

p

Dat

a / S

HE

RP

A

F. Krauss TUD




Jet spectra (2nd jet) in pp → Z + X @ Tevatron(D0-Note 5066)

20 40 60 80 100 120 140 160 180

1

10

210

310

20 40 60 80 100 120 140 160 180

1

10

210

310data w/stat errordata w/stat & sys errorPythia range statPythia range stat & sys


jet [GeV]nd 2T

p

Nr.

of E

vent

s

20 40 60 80 100 120 140 160 1800.2

1

234

jet [GeV]nd 2T

p

Dat

a / P

YTH

IA

20 40 60 80 100 120 140 160 180

1

10

210

310

20 40 60 80 100 120 140 160 180

1

10

210

310data w/stat errordata w/stat & sys errorSherpa range statSherpa range stat & sys


jet [GeV]nd 2T

p

Nr.

of E

vent

s

20 40 60 80 100 120 140 160 1800.2

1

234

jet [GeV]nd 2T

p

Dat

a / S

HE

RP

A

F. Krauss TUD




Jet spectra (3rd jet) in pp → Z + X @ Tevatron(D0-Note 5066)

20 40 60 80 100 120

1

10

210

20 40 60 80 100 120

1

10

210



jet [GeV]rd 3T

p

Nr.

of E

vent

s

20 40 60 80 100 1200.2

1

234

jet [GeV]rd 3T

p

Dat

a / P

YTH

IA

20 40 60 80 100 120

1

10

210

20 40 60 80 100 120

1

10

210



jet [GeV]rd 3T

p

Nr.

of E

vent

s

20 40 60 80 100 1200.2

1

2345

jet [GeV]rd 3T

p

Dat

a / S

HE

RP

A

F. Krauss TUD




φ1.jet,2.jet in pp → Z + X @ Tevatron(D0-Note 5066)

0 0.5 1 1.5 2 2.5 30

50

100

150

200

250

0 0.5 1 1.5 2 2.5 30

50

100

150

200

250 data w/stat errordata w/stat & sys errorPythia range statPythia range stat & sys


(jet,jet)φ ∆

Nr.

of E

vent

s

0 0.5 1 1.5 2 2.5 30.2

1

234

(jet,jet)φ ∆

Dat

a / P

YTH

IA

0 0.5 1 1.5 2 2.5 30

50

100

150

200

250

0 0.5 1 1.5 2 2.5 30

50

100

150

200

250data w/stat errordata w/stat & sys errorSherpa range statSherpa range stat & sys


(jet,jet)φ ∆

Nr.

of E

vent

s

0 0.5 1 1.5 2 2.5 30.2

1

234

(jet,jet)φ ∆

Dat

a / S

HE

RP

A

F. Krauss TUD




Extrapolation to LHC: pZ ,jet⊥

in Z+ jets

0 50 100 150 200 250 300 350 400pT (e+e-) [GeV]

10-6

10-5

10-4

10-3

10-2

10-1

1/σ

dσ/d

p T [1

/GeV

]

MC@NLOPythiaSherpa

e+e- + X @ LHC

50 100 150 200 250 300pT (first jet) [GeV]

10-3

10-2

10-1

100

101

dσ/d

p T [p

b/G

eV]

MC@NLOPythiaSherpaSherpa 2jet

e+e- + X @ LHC

F. Krauss TUD




Extrapolation to LHC: pZ ,jet⊥

in Z+ jets

20 40 60 80 100 120 140pT (second jet) [GeV]

10-3

10-2

10-1

100

101

dσ/d

p T [p

b/G

eV]

MC@NLOPythiaSherpaSherpa 2jet

e+e- + X @ LHC

20 40 60 80 100pT (third jet) [GeV]

10-3

10-2

10-1

100

dσ/d

p T [p

b/G

eV]

MC@NLOPythiaSherpaSherpa 2jetSherpa 3jet

e+e- + X @ LHC

F. Krauss TUD



Underlying event

In the following: Data from CDF, PRD 65 (2002) 092002, plots partially from C.Buttar

Multiple parton interactionsModel:

Incoherent parton-parton interactions

Ordered in p⊥ (or similar)=⇒ scales with Q2 of hard interaction

Proton AntiProton

Multiple Parton Interactions

PT(hard)

Outgoing Parton

Outgoing Parton

Underlying EventUnderlying Event

Observables

Charged Jet #1

Direction

∆φ∆φ∆φ∆φ

�Transverse� �Transverse�

�Toward�

�Away�

�Toward-Side� Jet

�Away-Side� Jet

F. Krauss TUD



Underlying event

Compare 3 regionsNchg versus PT(charged jet#1)

0

2

4

6

8

10

12

0 5 10 15 20 25 30 35 40 45 50

PT(charged jet#1) (GeV/c)

<N

ch

g>

in

1 G

eV

/c b

in

1.8 TeV |ηηηη|<1.0 PT>0.5 GeV/c

"Toward"

"Away"

"Transverse"

CDFdata uncorrected

PTsum versus PT(charged jet#1)

0

10

20

30

40

50

0 5 10 15 20 25 30 35 40 45 50

PT(charged jet#1) (GeV/c)

<P

Ts

um

> (

Ge

V/c

) in

1 G

eV

/c b

in

1.8 TeV |ηηηη|<1.0 PT>0.5 GeV/c

"Toward"

"Away"

"Transverse"

CDFdata uncorrected

p⊥ @ Tevatron, transverse

"Transverse" PT Distribution (charged)

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

0 2 4 6 8 10 12 14

PT(charged) (GeV/c)

dN

ch

g/d

PT

(1

/Ge

V/c

)

CDFdata uncorrected

1.8 TeV |ηηηη|<1

PT(chgjet1) > 2 GeV/c



F. Krauss TUD



Underlying event

From Tevatron . . .

2

4

6

8

CDF data

PYTHIA6.214 - ATLAS (CTEQ5L)

JIMMY4.1 - UE (CTEQ6L)

PYTHIA6.323 - UE (CTEQ6L)

< N

chg >

- tr

ansv

erse

reg

ion

0

1

2

0 10 20 30 40 50Pt leading jet (GeV)

Rat

io (M

C/d

ata)

. . . to LHC . . .

2

4

6

8

10

12

0 10 20 30 40 50

CDF data

PYTHIA6.214 - ATLAS (CTEQ5L)

JIMMY4.1 - UE (CTEQ6L)

PYTHIA6.323 - UE (CTEQ6L)

LHC prediction

Pt leading jet (GeV)

< N

chg >

- tr

ansv

erse

reg

ion

F. Krauss TUD



Underlying event

. . . or is it rather this?

F. Krauss TUD



SummarySuccess of LHC probably depends on detailedunderstanding of QCD

The first few years of LHC running are a great time forQCD-addicts

There is leeway for an improved understanding of QCD -on all levels between theory and experiment

There are still puzzles and problems to be resolved -

from technicalities:sufficient precision in PDFs, jets and their definitions,multijets (a personal selection)to basics:underlying event, interplay of soft & hard QCD . . .

F. Krauss TUD


Documents

High-Q2 physics at the LHC · High-Q2 physics at the LHC Frank Krauss Institute for Theoretical Physics TU Dresden DIS 2006, KEK, 23.4.2006 F. Krauss TUD ... H1 150 < Q2 < 200 GeV2