Benchmark QCD Measurements and Tools at ATLAS

Benchmark QCD Measurements and Tools at ATLAS

Craig Buttar

University of Glasgow

Craig Buttar, CTEQ07 Michigan May 2007 2

Outline

• Soft physics: minimum bias and underlying event• Measurements of PDFs for precision physics and BSM• Jet algorithms and multijets

Low pt physics


Why measure min bias?

Not exactly what the LHC was built for!But….. • Physics: measure dN/d|=0

– Compare to NSD data from SppS and Tevatron

• MB samples for pile-up studies– Calorimeter– Physics analyses– Benchmark for sLHC

• Overlap with underlying events– analyses eg VBF, Jets…

• Demonstrate that ATLAS is operational• Inter-calibration of detector elements

– Uniform events• Alignment • Baseline for heavy ions


MBTS

• Trigger scintillation counters mounted on end of LAr calorimeter covering same radii as ID

ηη=2.0=2.0

ηη=3.8=3.8interactiointeraction pointn point

Beam-pipeBeam-pipe

++ηη

pan

nel

pan

nel

MBTMBTSS

UA5• To compare to UA5 and CDF data

need to understand composition of the sample trigger bias

• Currently generate inelastic and diffractive parts using PYTHIA

– Need to investigate other simulations-PHOJET


Minimum bias measurements

Solve low pt tracking ie down to ~100MeV M.Leyton


number of interactionsn

zn

MB measurements?

Uniformgaussian

d-gaussian

Can we measure such distributions over limited rapidity coverage ||<2.5?• Charged multiplicities• N vs <pt>• dN/dpt

•MC simulation to map physics -- > trigger

• MBTS 2<||<4 single diff+double diff+non-diff

•Required to compare to UA5 etc


The underlying eventTra

nsvers

e <

Nch

g >

PYTHIA6.214 - tuned

PHOJET1.12

x 3

LHC

x1.5

Extrapolation of UE to LHC is unknownDepends on• Multiple interactions• Radiation• PDFs• Striing formation

High PT scatter

Beam remnants

ISR

• Lepton isolation • Top• Jet energy• VBF

Craig Buttar, CTEQ07 Michigan May 2007 9Rat

io <

NT

rac

kR

ec

o>

/<N

Tra

ck

MC>

Leading jet ET (GeV)

Reconstructing the underlying event

CDF Run 1 underlying event analysisPhys. Rev. D, 65 092002 (2002)

Njets > 1, |ηjet| < 2.5, ETjet >10 GeV,

|ηtrack | < 2.5, pTtrack > 1.0 GeV/c

A.Moraes


Underlying event for different processes

• The underlying event for electroweak processes needs to be studied– Critical for Higgs search in

VBF

Charged Particle Density: dN/dd

0.1

1.0

10.0

0 30 60 90 120 150 180 210 240 270 300 330 360

(degrees)C

ha

rge

d P

art

icle

De

ns

ity

Leading Jet

Leading Photon

Z-boson

RDF PreliminaryPYTHIA Tune A

Charged Particles (||<1.0, PT>0.5 GeV/c)

"Transverse" Region

Jet#1PhotonZ-boson

Jet #1 Direction

“Toward”

“Transverse” “Transverse”

“Away”

Photon #1 Direction

“Toward”


“Away”

Z-boson Direction

“Toward”


“Away”

R.Field


Resolving hard and soft components

Jet #1 Direction

“Toward”

“TransMAX” “TransMIN”

“Away”

Jet #1 Direction

“Toward”

“TransMAX” “TransMIN”

Jet #2 Direction

“Away”

“Leading Jet”

“Back-to-Back”

•TransMAX and transMIN sensitive to radiation and soft UE respectively •Back-to-back sample suppresses radiation• difference between tranMAX region and transMIN in leading jet and b-2-b jet sample


Parton Level Calibration: Jet Algorithms in Pt Balance

Biases on pT balance MOP for the different jet algorithms:

Too close to the generation cut

Algorithms Cone 0.7 Cone 0.4 Kt (D=1)

Parton level -1 - 0% -1 - 0% -1 - 0%

Particle level 1 - 0% -6 - -3% 6 - 1%

Recon level -2 - 0% -15 - -7% 7 - 2%

(pTγ+pTparton)/2

(pTγ+pTparton)/2

(pTγ+pTparton)/2

Cone 0.4 collects only the core of the jet

Leakage out of cone and UE compensate in cone 0.7

Excess of energy in Kt jets (D=1) due to UE and noise

cone 0.4 cone 0.7

Kt

Differences between recon and particle levels related to the standard H1 weighting (calibrated for cone 0.7)

S.Jorgensen


Extrapolation to LHC energies

Tra

nsv

erse

< N

chg >

Pt (leading jet in GeV)TevatronTevatron

LHCLHC

x3

x5

x4

Tra

nsvers

e <

Nch

g >

PYTHIA6.214 - tuned

PHOJET1.12

Pt (leading jet in GeV)

x 3

LHC

x1.5

No agreement amongst MCEnergy extrapolation is a tunable parameter

. 6

0

0 1

1.9GeV1TeVt

sp

A.Moraes


Simulation of underlying event

• MC tools for simulation of underlying event– PYTHIA (UE+min bias)

– Herwig + Jimmy (UE only, pt-cut)

– PHOJET (Min bias and UE)

• All give a reasonable description of Tevatron data with tuning (pt-min, matter distributions)

• Energy extrapolation is essentially a free parameter and uncertain data required

• SHERPA also has simulation of underlying event but has been studied less


Impact of PDF uncertainty on new physics

Similarly PDF uncertainties limits the sensitivity in inclusive xsect to BSM physics

Extra-dimensions affect the di-jet cross section through the running of s. Parameterised by number of extra dimensions D and compactification scale Mc.

PDF uncertainties (mainly due to high-x gluon) reduce sensitivity to compactification scale from ~5 TeV to 2 TeV

2XD

4XD

6XD

SM

Mc= 2 TeV

uncertainties

PDF

Mc= 2 TeV Mc= 6 TeV

S.Ferrag


Measure high-x gluon pdfs from inclusive jet cross-section

• Measure inclusive xsect to get high-x gluons

• Measure in different rapidity bins– New physics vs pdf

• Theoretical uncertainties in QCD calculation– Scale dependence

– PDF uncertainty

– Use NLOJET++ and CTEQ via LHAPDF

• Experimental errors– Jet energy scale


Uncertainty due to high-x gluon PDF

At 1TeV in central region error is 10-15%

NLOJET++/CTEQ6.1(29+30)Other pdfscontributeAt low pt

(NLO)

D.Clements


Scale errors

5%-10% scale error.

From changing scale µr=µf from 0.5pT jet to 2.0pT jet

D.Clements


Experimental Errors

10% JES 6% on

5% JES 30% on

1% JES 6% on

D.Clements

JES can measued to ~1% using Wjj in top events, can also use -j, Z-j etcBut need to “bootstrap” from ~500GeV to ≥ 1TeV region


Checking JES uncertainty at high Et

Truth jets

Reconstructed

Bootstrap JES from 1% measured at low Et with Wjj in top, -jet to high Et using jet-balancing•Truth jets

Can identify 1% change in JES with increasing Et

•ReconstructionHarder to see 1% due to resolution effect


Analysis – Constraining High x-Gluon

Effect of adding simulated ATLAS collider data to gluon uncertainty in a global PDF fit (C. Gwenlan)

x x

Glu

on u

ncer

tain

ty

•A very good control (1%) of the Jet Energy Scale is needed in order to constrain PDFs using collider data.


At the LHC we will have dominantly sea-sea parton interactions at low-xAnd at Q2~M2

W/Z the sea is driven by the gluon by the flavour blind g ->qqgluon is far less precisely determined for all x values

Measurement of W and Z rapidity distributions can improve our knowledge of the gluon PDF key to using W,Z as luminosity monitor

_

Improving low-x gluon using rapidity distribution in W-decay

2.5

Cooper-Sarkar, Tricoli


At y=0 the total W PDF uncertainty is ~ ±5.2% from ZEUS-S~ ±3.6% from MRST01E~ ±8.7% from CTEQ6.1MZEUS to MRST01 central value difference ~5%ZEUS to CTEQ6.1 central value difference ~3.5% (From LHAPDF eigenvectors)

W and Z Rapidity Distributions for different PDFs

CTEQ6.1M MRST02 ZEUS-S

Analytic calculations: Error bands are the full PDF Uncertainties

GOAL: syst. exp. error ~3-5%

CTEQ6.1M



PDF constraining potential of ATLAS

Effect of including the ATLAS W Rapidity “pseudo-data” in global PDF Fits: how much can we reduce the PDF errors when LHC is up and running?

Simulate real experimental conditions:Generate 1M “data” sample with CTEQ6.1 PDF with ATLFAST detector simulationinclude this pseudo-data (with imposed 4% error) in the global ZEUS PDF fit (with Det.->Gen. level correction).Central value of ZEUS-PDF prediction shifts and uncertainty is reduced:low-x gluon shape parameter λ, xg(x) ~ x –λ BEFORE λ = -0.199 ± 0.046AFTER λ = -0.181 ± 0.030 35% improvement

ZEUS-PDF BEFORE including W data

e+ CTEQ6.1 pseudo-data

ZEUS-PDF AFTER including W data

e+ CTEQ6.1 pseudo-data



Jet Finders in ATLAS: Implementations

• General implementation– All jet finders can run on all navigable ATLAS data objects providing a 4-

momentum through the standard interface– Tasks common to different jet finders are coded only once

• Different jet finders use the same tools

– Default full 4-momentum recombination• Following Tevatron recommendation

• Cone jets– Seeded fixed cone finder

• Iterative cone finder starting from seeds• Free parameters are: seed Et threshold (typically 1 GeV) and cone size R• Needs split and merge with overlap fraction threshold of 50%

– Seedless cone finder• Theoretically ideal but practically prohibitive

– Each input is a seed– New fast implementation in sight: G.P.Salam & Gregory Soyez, A practical seedless infrared

safe cone jet algorithm,arXiv:0704.0292

• No split and merge needed

– MidPoint cone • Seeded cone places seeds between two large signals• Still needs split and merge


Jet Finders in ATLAS: Implementations • Dynamic Angular Distance Jet

Finders– Kt algorithm

• Fast implementation available → no pre-clustering to reduce number of input objects needed anymore

– “Aachen” algorithm• Similar to Kt, but only distance

between objects considered (no use of Pt)

– Optimal Jet Finder• Based on the idea of minimizing

a test function sensitive to event shape

• Uses unclustered energy in jet finding

CPU time(arb. units)

P.A.Delsart, (U. Montreal)ATLAS T&P WeekMarch 2006


Jet Finders in ATLAS: Algorithm Parameters• Adjust parameters to physics needs

– Mass spectroscopy W →jj in ttbar needs narrow jet

– Generally narrow jets preferred in busy final states like SUSY

– QCD jet cross section measurement prefers wider jets

• Important to capture all energy from the scattered parton

• Common configuration– ATLAS, CMS, theory

• J.Huston is driving this– Likely candidate two-pass mid-point

N.G

od

bh

an

e, Je

tRec

Ph

on

e C

on

f. Ju

ne 2

00

6

P.-A. Delsart, JetRec Phone Conf. June 28, 2006

mW

Algorithm Cone Size R Distance D Clients

Seeded Cone 0.4 W mass spectroscopy, top physicsKt 0.4

Seeded Cone 0.7QCD, jet cross-sections

Kt 0.6


Azimuthal dijet decorrelation

Early measurement to benchmark generators particularly parton showers/higher orders

2 dijet

dijet 2

dijet=

dijet~ 2

A.Moraes


Reconstructed di-jet azimuthal decorrelations

Selecting di-jet events:

300 < ETMAX < 600 GeV

Cone jet algorithm (R=0.7)Njets = 2, |ηjet| < 0.5, ET

jet #2 > 80 GeV,

Two analysis regions:

600 < ETMAX < 1200 GeV

J5J5

J6J6

A.Moraes


Multijets in top events

• MC@NLO and ALPGEN agree for hardest jet

• HERWIG fails at high pt • Significant number of events

have 3 additional jets there is a discrepancy between MC@NLO and HERWIG vs ALPGEN

measure multijet spectra • Possible with early high

energy running• Key for ttH

Spectra include tt

A.P.Colijn


Summary and conclusions

• QCD benchmarks (inc low-pt processes)– Underlying event

• Fundamental part of hadronic environment that needs to be understood

• Study soft and hard part

• Measure for different processes – QCD vs EW

– PDFs• New regime in PDFs

• Need to measure for precision SM and high-pt BSM physics

– Multijets• Measure azimuthal decorrelations to validate simulations

• Many more jets in events tt6J+nJ

• Need to understand multiplicities

Extra slides


Tevatron LHC

Q2

(GeV

)

tot


Low pt tracking efficiency and fake rates

M.Leyton


Minimum bias and Underlying Event: LHC predictions

Tevatron

● CDF 1.8 TeV

PYTHIA6.214 - tuned PYTHIA predictions

dN/dη (η=0)

Nch jet-pt=20GeV

1.8TeV (pp) 4.1 2.3

14TeV (pp) 7.0 7.0

increase ~x1.8 ~x3

~80%~200%

LHC prediction

Tevatron

PYTHIA6.214 - tuned

● CDF 1.8 TeV

LHC

MB onlyUE includes radiation and small impact parameter bias

dN/d in minimum bias events

Minimum bias = inelastic pp interactionUnderlying event = hadronic environment not part of the hard scatter

Pt leading jet (GeV)

Particle

density


Tracking in MB events

• Acceptance limited in rapidity and pt

• Rapidity coverage– Tracking covers ||<2.5

• pT problem

– Need to extrapolate by ~x2 Need to understand low pt

charge track reconstruction

1000 events1000 events

dNdNchch/d/d

dNdNchch/dp/dpTT

Black = Generated (Pythia6.2)

Blue = TrkTrack: iPatRec

Red = TrkTrack: xKalman

Reconstruct tracks with:Reconstruct tracks with: 1) pT>500MeV1) pT>500MeV 2) |d2) |d00| < 1mm| < 1mm 3) # B-layer hits >= 13) # B-layer hits >= 1 4) # precision hits >= 84) # precision hits >= 8

pT (MeV)


Minimum bias studies: Charged particle density at = 0

LHC?

Large uncertainties in predicted particle density in minimum bias events ~x2

Measurement with central tracker at level of ~10% with ~10k events – first data

Why? soft physics, pile-up at higher luminosities, calibration of experiment


Compare abrupt and smooth pt-cut-off:Abrupt cut-off generates a Poisson distribution with too few multi-parton interactions in a single event

Compare matter distributions:uniform, gaussian, double gaussian Use double gaussian

number of interactionsn

zn

Use MB multiplicity distributions to tune fluctuations in number of events

abrupt

smooth

Uniform

gaussian

d-gaussian


Herwig+Jimmy

• Jimmy is multi-parton interaction model similar to PYTHIA

• Main parameter is pt-min• Only for hard UE cannot

model low-pt ie MB difficult to get energy dependence

• Matter distribution is determined from em form factor

• Gives a good description of CDF data with increased pt-min2.53.25GeV


VBF Signal (HWWll)

•forward tagging jets

•correlated isolated leptons

• low hadronic activity in central region

•central Higgs production

Tagging jet

Tagging jet

H

W

WZ/WZ/W

Important discovery channelFor Higgs in mass range120-200GeV


Model Parameter

Simple MSTP(82)=1

PARP(81)=1.9

Complex MSTP(82)=4

PARP(82)=1.9

Tuned MSTP(82)=4

PARP(82)=1.8

PARP(84)=0.5

Model CJV (eff) LEPACC (eff) All Vetoes (eff)

Simple 0.943 ± 0.003 0.610 ±0.001 0.084 ±0.001

Complex 0.885 ± 0.003 0.575 ±0.001 0.076 ±0.001

Tuned 0.915 ± 0.003 0.589 ±0.001 0.080 ±0.001

Uncertainty at the level of ~6% on CJV Pt>20GeV and ~3% on leptonGiving a total uncertaintly in the range ~8%


• Effect of UE on lepton efficiency• Vary pt-min by 3• Determine from data

• Good muons– Barrel <1.1 Pt>7GeV– Endcap 1.1<<2.4 P>9GeV

• Isolation Pt for charged tracks excluding s

with Pt>0.8GeV and R<0.3 around in - space

S.Abdullin et al (CMS) Les Houches 05

Lepton isolation in H->4

Process Event eff Default Event eff –3 Event eff +3

H4 MH=150GeV 0.775 ± 0.004 0.707 ± 0.005 0.812 ± 0.004

ZZ background 0.780 ± 0.004 0.721 ± 0.005 0.838 ± 0.004

4 random Z-inclusive 0.762 ± 0.007 0.706 ± 0.007 0.821 ±0.006


Extract effect of UE from data

• Use inclusive Z-sample, high statistics• Similar dependence to ZZ sample but small systematic shift

random

UE


Impact of decreasing experimental systematic uncertainty-uncorrelated


JES extrapolation

Truth jets

Reconstructed

Bootstrap JES to high Et using jet-balancing•Truth jets

Can identify 1% change in JES with increasing Et

•ReconstructionHarder to see 1% due to resolution effect


Impact of decreasing experimental correlated systematic uncertainty

Challenging!

Can we decrease Jet Energy Scale systematic to 1%?


Jet Algorithm Choices: Guidelines for ATLAS• Initial considerations

– Jets define the hadronic final state of basically all physics channels

• Jet reconstruction essential for signal and background definition

• Applied algorithms not necessarily universal for all physics scenarios

– Which jet algorithms to use?• Use theoretical and experimental

guidelines collected by the Run II Tevatron Jet Physics Working Group

– J.Blazey et al., hep-ex/0005012v2 (2000)

• Theoretical requirements– Infrared safety

• Artificial split due to absence of gluon radiation between two partons/particles

– Collinear safety• Miss jet due to signal split into two towers

below threshold• Sensitivity due to Et ordering of seeds

– Invariance under boost• Same jets in lab frame of reference as in

collision frame– Order independence

• Same jet from partons, particles, detector signals

infrared sensitivity(artificial split in absence of soft gluon radiation)

collinear sensitivity (1)(signal split into two towers below threshold)

collinear sensitivity (2)(sensitive to Et ordering of seeds)


Jet Algorithms: Experimental Requirements

• Detector technology independence– Jet efficiency should not depend on detector technology

• Final jet calibration and corrections ideally unfolds all detector effects

• Minimal contribution from spatial and energy resolution to reconstructed jet kinematics

– Unavoidable intrinsic detector limitations set limits• Stability within environment

– (Electronic) detector noise should not affect jet reconstruction within reasonable limits • Energy resolution limitation• Avoid energy scale shift due to noise

– Stability with changing (instantaneous) luminosity• Control of underlying event and pile-up signal contribution

• “Easy” to calibrate– Small algorithm bias for jet signal

• High reconstruction efficiency– Identify all physically interesting jets from energetic partons in perturbative QCD– Jet reconstruction in resonance decays

• High efficiency to separate close-by jets from same particle decay• Least sensitivity to boost of particle

• Efficient use of computing resources– Balance physics requirements with available computing

• Fully specified algorithms only– Absolutely need to compare to theory at particle and parton level– Pre-clustering strategy, energy/direction definitions, recombination rules, splitting and

merging strategy if applicable


Jet Finders in ATLAS: Implementations

• General implementation– All jet finders can run on all navigable ATLAS data objects providing a 4-

momentum through the standard interface– Tasks common to different jet finders are coded only once

• Different jet finders use the same tools

– Default full 4-momentum recombination• Following Tevatron recommendation

• Cone jets– Seeded fixed cone finder

• Iterative cone finder starting from seeds• Free parameters are: seed Et threshold (typically 1 GeV) and cone size R• Needs split and merge with overlap fraction threshold of 50%

– Seedless cone finder• Theoretically ideal but practically prohibitive

– Each input is a seed– New fast implementation in sight: G.P.Salam & Gregory Soyez, A practical seedless infrared

safe cone jet algorithm,arXiv:0704.0292

• No split and merge needed

– MidPoint cone • Seeded cone places seeds between two large signals• Still needs split and merge


Jet Finders in ATLAS: Implementations • Dynamic Angular Distance Jet

Finders– Kt algorithm

• Fast implementation available → no pre-clustering to reduce number of input objects needed anymore

– “Aachen” algorithm• Similar to Kt, but only distance

between objects considered (no use of Pt)

– Optimal Jet Finder• Based on the idea of minimizing

a test function sensitive to event shape

• Uses unclustered energy in jet finding

CPU time(arb. units)

P.A.Delsart, (U. Montreal)ATLAS T&P WeekMarch 2006


Jet Finders in ATLAS: Algorithm Parameters• Adjust parameters to physics needs

– Mass spectroscopy W →jj in ttbar needs narrow jet

– Generally narrow jets preferred in busy final states like SUSY

– QCD jet cross section measurement prefers wider jets

• Important to capture all energy from the scattered parton

• Common configuration– ATLAS, CMS, theory

• J.Huston is driving this– Likely candidate two-pass mid-point

N.G

od

bh

an

e, Je

tRec

Ph

on

e C

on

f. Ju

ne 2

00

6

P.-A. Delsart, JetRec Phone Conf. June 28, 2006

mW

Algorithm Cone Size R Distance D Clients

Seeded Cone 0.4 W mass spectroscopy, top physicsKt 0.4

Seeded Cone 0.7QCD, jet cross-sections

Kt 0.6


ATLAS Jet Reconstruction and Calibration

• Contributions to the jet signal:

• Try to address reconstruction and calibration through different levels of factorization

physics reaction of interest (parton level)

lost soft tracks due to magnetic field

added tracks from underlying event

jet reconstruction algorithm efficiency

detector response characteristics (e/h ≠ 1)

electronic noise

dead material losses (front, cracks, transitions…)

pile-up noise from (off-time) bunch crossings

detector signal inefficiencies (dead channels, HV…)

longitudinal energy leakage

calo signal definition (clustering, noise suppression ,…)

jet reconstruction algorithm efficiency

added tracks from in-time (same trigger) pile-up event


Effect of multijets on inclusive SUSY studies

Benchmark QCD Measurements and Tools at ATLAS

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